Overview

Dataset statistics

Number of variables66
Number of observations516490
Missing cells13253159
Missing cells (%)38.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory260.1 MiB
Average record size in memory528.0 B

Variable types

Categorical31
Unsupported20
Numeric15

Alerts

Centro has constant value ""Constant
Classificação contábil múltipla has constant value ""Constant
Código entrada has constant value ""Constant
Divisão ord.cliente has constant value ""Constant
Empresa has constant value ""Constant
Item gerado automaticamente has constant value ""Constant
Moeda has constant value ""Constant
Operação has constant value ""Constant
Data de lançamento has a high cardinality: 410 distinct valuesHigh cardinality
Centro custo has a high cardinality: 78 distinct valuesHigh cardinality
Elemento PEP has a high cardinality: 60 distinct valuesHigh cardinality
Hora do registro has a high cardinality: 92767 distinct valuesHigh cardinality
Imobilizado has a high cardinality: 56 distinct valuesHigh cardinality
Lote has a high cardinality: 50684 distinct valuesHigh cardinality
Montante em MI has a high cardinality: 156915 distinct valuesHigh cardinality
Txt.tipo movimento has a high cardinality: 104 distinct valuesHigh cardinality
Doc.material is highly overall correlated with Ordem do cliente.1 and 14 other fieldsHigh correlation
Item doc.material is highly overall correlated with Descrição motivo movimento and 7 other fieldsHigh correlation
Cliente is highly overall correlated with Estoque especial and 3 other fieldsHigh correlation
Contador is highly overall correlated with Nº do roteiro operações and 7 other fieldsHigh correlation
Item is highly overall correlated with Estoque especial and 2 other fieldsHigh correlation
Item ord.cliente is highly overall correlated with Item ord.cliente.1 and 7 other fieldsHigh correlation
Item ord.cliente.1 is highly overall correlated with Item ord.cliente and 6 other fieldsHigh correlation
Linha original is highly overall correlated with Descrição motivo movimento and 4 other fieldsHigh correlation
Material Alterado is highly overall correlated with Item ord.cliente.1 and 4 other fieldsHigh correlation
Motivo do movimento is highly overall correlated with Descrição motivo movimento and 10 other fieldsHigh correlation
Nº do roteiro operações is highly overall correlated with Contador and 11 other fieldsHigh correlation
Nº item reserva transferência is highly overall correlated with Nº reserva and 8 other fieldsHigh correlation
Nº reserva is highly overall correlated with Nº item reserva transferência and 8 other fieldsHigh correlation
Ordem do cliente.1 is highly overall correlated with Doc.material and 8 other fieldsHigh correlation
Pedido is highly overall correlated with Depósito and 8 other fieldsHigh correlation
Descrição motivo movimento is highly overall correlated with Doc.material and 17 other fieldsHigh correlation
Depósito is highly overall correlated with Linha original and 11 other fieldsHigh correlation
Estoque especial is highly overall correlated with Doc.material and 21 other fieldsHigh correlation
UM registro is highly overall correlated with Doc.material and 8 other fieldsHigh correlation
Ano doc.material is highly overall correlated with Item doc.material and 3 other fieldsHigh correlation
Centro custo is highly overall correlated with Doc.material and 13 other fieldsHigh correlation
Cód.débito/crédito is highly overall correlated with Descrição motivo movimento and 2 other fieldsHigh correlation
Código de movimento is highly overall correlated with Doc.material and 21 other fieldsHigh correlation
Consumo is highly overall correlated with Doc.material and 14 other fieldsHigh correlation
Elemento PEP is highly overall correlated with Doc.material and 18 other fieldsHigh correlation
Imobilizado is highly overall correlated with Doc.material and 16 other fieldsHigh correlation
Classificação is highly overall correlated with Material Alterado and 9 other fieldsHigh correlation
Subnº is highly overall correlated with Doc.material and 20 other fieldsHigh correlation
Texto cabeçalho documento is highly overall correlated with Doc.material and 15 other fieldsHigh correlation
Tipo de operação is highly overall correlated with Doc.material and 12 other fieldsHigh correlation
UM pedido is highly overall correlated with Doc.material and 16 other fieldsHigh correlation
Unid.medida básica is highly overall correlated with Doc.material and 7 other fieldsHigh correlation
Unid.prç.pedido is highly overall correlated with Doc.material and 17 other fieldsHigh correlation
Estoque especial is highly imbalanced (94.8%)Imbalance
UM registro is highly imbalanced (84.4%)Imbalance
Ano doc.material is highly imbalanced (54.4%)Imbalance
Consumo is highly imbalanced (80.3%)Imbalance
Imobilizado is highly imbalanced (76.8%)Imbalance
Texto cabeçalho documento is highly imbalanced (77.6%)Imbalance
UM pedido is highly imbalanced (73.5%)Imbalance
Unid.medida básica is highly imbalanced (84.6%)Imbalance
Descrição motivo movimento has 516202 (99.9%) missing valuesMissing
Depósito has 21000 (4.1%) missing valuesMissing
Estoque especial has 330355 (64.0%) missing valuesMissing
Centro custo has 486352 (94.2%) missing valuesMissing
Classificação contábil múltipla has 516103 (99.9%) missing valuesMissing
Cliente has 486079 (94.1%) missing valuesMissing
Código de movimento has 389045 (75.3%) missing valuesMissing
Código entrada has 496147 (96.1%) missing valuesMissing
Consumo has 500295 (96.9%) missing valuesMissing
Diagrama de rede has 510921 (98.9%) missing valuesMissing
Documento do depósito has 516490 (100.0%) missing valuesMissing
Elemento PEP has 515621 (99.8%) missing valuesMissing
Fornecedor has 479220 (92.8%) missing valuesMissing
Imobilizado has 516042 (99.9%) missing valuesMissing
Item gerado automaticamente has 403344 (78.1%) missing valuesMissing
Lote has 162644 (31.5%) missing valuesMissing
Nº do depósito has 516490 (100.0%) missing valuesMissing
nº do documento configurável has 516490 (100.0%) missing valuesMissing
Nota acomp.mercadoria has 516490 (100.0%) missing valuesMissing
Operação has 516476 (> 99.9%) missing valuesMissing
Ordem has 222222 (43.0%) missing valuesMissing
Ordem do cliente has 359478 (69.6%) missing valuesMissing
Ordem do cliente.1 has 331458 (64.2%) missing valuesMissing
Pedido has 465255 (90.1%) missing valuesMissing
Segmento de estoque has 516490 (100.0%) missing valuesMissing
Subnº has 516042 (99.9%) missing valuesMissing
Texto cabeçalho documento has 507032 (98.2%) missing valuesMissing
Tipo de avaliação has 516490 (100.0%) missing valuesMissing
UM pedido has 418392 (81.0%) missing valuesMissing
Unid.prç.pedido has 486840 (94.3%) missing valuesMissing
Linha original is highly skewed (γ1 = 227.2192176)Skewed
Motivo do movimento is highly skewed (γ1 = 139.650647)Skewed
Nº do roteiro operações is highly skewed (γ1 = 53.53880339)Skewed
Nº item reserva transferência is highly skewed (γ1 = 28.85275293)Skewed
Tipo de movimento is an unsupported type, check if it needs cleaning or further analysisUnsupported
Qtd. UM registro is an unsupported type, check if it needs cleaning or further analysisUnsupported
Data de entrada is an unsupported type, check if it needs cleaning or further analysisUnsupported
Data do documento is an unsupported type, check if it needs cleaning or further analysisUnsupported
Diagrama de rede is an unsupported type, check if it needs cleaning or further analysisUnsupported
Documento do depósito is an unsupported type, check if it needs cleaning or further analysisUnsupported
Fornecedor is an unsupported type, check if it needs cleaning or further analysisUnsupported
Montante externo em MI is an unsupported type, check if it needs cleaning or further analysisUnsupported
Nº do depósito is an unsupported type, check if it needs cleaning or further analysisUnsupported
nº do documento configurável is an unsupported type, check if it needs cleaning or further analysisUnsupported
Nota acomp.mercadoria is an unsupported type, check if it needs cleaning or further analysisUnsupported
Ordem is an unsupported type, check if it needs cleaning or further analysisUnsupported
Ordem do cliente is an unsupported type, check if it needs cleaning or further analysisUnsupported
Qtd.em UM pedido is an unsupported type, check if it needs cleaning or further analysisUnsupported
Qtd.em UPP is an unsupported type, check if it needs cleaning or further analysisUnsupported
Quantidade is an unsupported type, check if it needs cleaning or further analysisUnsupported
Segmento de estoque is an unsupported type, check if it needs cleaning or further analysisUnsupported
Tipo de avaliação is an unsupported type, check if it needs cleaning or further analysisUnsupported
Valor de venda is an unsupported type, check if it needs cleaning or further analysisUnsupported
Valor PV com IVA is an unsupported type, check if it needs cleaning or further analysisUnsupported
Contador has 250126 (48.4%) zerosZeros
Item has 465255 (90.1%) zerosZeros
Item ord.cliente has 359865 (69.7%) zerosZeros
Item ord.cliente.1 has 331458 (64.2%) zerosZeros
Linha original has 516103 (99.9%) zerosZeros
Motivo do movimento has 516202 (99.9%) zerosZeros
Nº do roteiro operações has 338514 (65.5%) zerosZeros
Nº item reserva transferência has 386941 (74.9%) zerosZeros
Nº reserva has 386941 (74.9%) zerosZeros

Reproduction

Analysis started2023-04-01 18:14:55.652912
Analysis finished2023-04-01 18:24:29.587067
Duration9 minutes and 33.93 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Descrição motivo movimento
Categorical

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)7.3%
Missing516202
Missing (%)99.9%
Memory size3.9 MiB
FINAL DE BOBINA
66 
INÍCIO DE BOBINA
42 
REGULAGEM DO EQUIPAM
40 
INICIO DE BOBINA
27 
REFILE CORTE LONGITU
25 
Other values (16)
88 

Length

Max length20
Median length19
Mean length15.614583
Min length5

Characters and Unicode

Total characters4497
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)2.8%

Sample

1st rowFraca qualidade
2nd rowFraca qualidade
3rd rowIncompleto
4th rowIncompleto
5th rowIncompleto

Common Values

ValueCountFrequency (%)
FINAL DE BOBINA 66
 
< 0.1%
INÍCIO DE BOBINA 42
 
< 0.1%
REGULAGEM DO EQUIPAM 40
 
< 0.1%
INICIO DE BOBINA 27
 
< 0.1%
REFILE CORTE LONGITU 25
 
< 0.1%
Remessa incorreta 25
 
< 0.1%
Incompleto 16
 
< 0.1%
Fraca qualidade 15
 
< 0.1%
ESQUELETO 10
 
< 0.1%
RISCOS 7
 
< 0.1%
Other values (11) 15
 
< 0.1%
(Missing) 516202
99.9%

Length

2023-04-01T15:24:30.970971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 139
18.9%
bobina 135
18.3%
final 66
9.0%
início 42
 
5.7%
regulagem 40
 
5.4%
do 40
 
5.4%
equipam 40
 
5.4%
refile 28
 
3.8%
inicio 27
 
3.7%
corte 25
 
3.4%
Other values (22) 155
21.0%

Most occurring characters

ValueCountFrequency (%)
I 492
 
10.9%
449
 
10.0%
E 380
 
8.5%
O 319
 
7.1%
N 303
 
6.7%
A 303
 
6.7%
B 270
 
6.0%
D 186
 
4.1%
L 171
 
3.8%
R 130
 
2.9%
Other values (30) 1494
33.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3321
73.8%
Lowercase Letter 727
 
16.2%
Space Separator 449
 
10.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 492
14.8%
E 380
11.4%
O 319
9.6%
N 303
9.1%
A 303
9.1%
B 270
8.1%
D 186
 
5.6%
L 171
 
5.1%
R 130
 
3.9%
U 120
 
3.6%
Other values (13) 647
19.5%
Lowercase Letter
ValueCountFrequency (%)
a 112
15.4%
e 106
14.6%
r 66
9.1%
o 59
8.1%
c 57
7.8%
s 50
6.9%
t 42
 
5.8%
i 42
 
5.8%
n 42
 
5.8%
m 41
 
5.6%
Other values (6) 110
15.1%
Space Separator
ValueCountFrequency (%)
449
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4048
90.0%
Common 449
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 492
 
12.2%
E 380
 
9.4%
O 319
 
7.9%
N 303
 
7.5%
A 303
 
7.5%
B 270
 
6.7%
D 186
 
4.6%
L 171
 
4.2%
R 130
 
3.2%
U 120
 
3.0%
Other values (29) 1374
33.9%
Common
ValueCountFrequency (%)
449
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4453
99.0%
None 44
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 492
 
11.0%
449
 
10.1%
E 380
 
8.5%
O 319
 
7.2%
N 303
 
6.8%
A 303
 
6.8%
B 270
 
6.1%
D 186
 
4.2%
L 171
 
3.8%
R 130
 
2.9%
Other values (27) 1450
32.6%
None
ValueCountFrequency (%)
Í 42
95.5%
Ç 1
 
2.3%
à 1
 
2.3%

Centro
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
A001
516490 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2065960
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA001
2nd rowA001
3rd rowA001
4th rowA001
5th rowA001

Common Values

ValueCountFrequency (%)
A001 516490
100.0%

Length

2023-04-01T15:24:31.052054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:31.873297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
a001 516490
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1032980
50.0%
A 516490
25.0%
1 516490
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1549470
75.0%
Uppercase Letter 516490
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1032980
66.7%
1 516490
33.3%
Uppercase Letter
ValueCountFrequency (%)
A 516490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1549470
75.0%
Latin 516490
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1032980
66.7%
1 516490
33.3%
Latin
ValueCountFrequency (%)
A 516490
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2065960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1032980
50.0%
A 516490
25.0%
1 516490
25.0%

Depósito
Categorical

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)< 0.1%
Missing21000
Missing (%)4.1%
Memory size3.9 MiB
P008
150224 
P001
99481 
P007
66203 
P006
51838 
P010
34525 
Other values (20)
93219 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1981960
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP004
2nd rowP004
3rd rowP004
4th rowP004
5th rowP004

Common Values

ValueCountFrequency (%)
P008 150224
29.1%
P001 99481
19.3%
P007 66203
12.8%
P006 51838
 
10.0%
P010 34525
 
6.7%
P022 16120
 
3.1%
P012 16027
 
3.1%
P004 12616
 
2.4%
P020 11110
 
2.2%
P003 10385
 
2.0%
Other values (15) 26961
 
5.2%
(Missing) 21000
 
4.1%

Length

2023-04-01T15:24:31.934933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p008 150224
30.3%
p001 99481
20.1%
p007 66203
13.4%
p006 51838
 
10.5%
p010 34525
 
7.0%
p022 16120
 
3.3%
p012 16027
 
3.2%
p004 12616
 
2.5%
p020 11110
 
2.2%
p003 10385
 
2.1%
Other values (15) 26961
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 941287
47.5%
P 495490
25.0%
1 154899
 
7.8%
8 151155
 
7.6%
2 70245
 
3.5%
7 66429
 
3.4%
6 60385
 
3.0%
3 18217
 
0.9%
4 13507
 
0.7%
5 9411
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1486470
75.0%
Uppercase Letter 495490
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 941287
63.3%
1 154899
 
10.4%
8 151155
 
10.2%
2 70245
 
4.7%
7 66429
 
4.5%
6 60385
 
4.1%
3 18217
 
1.2%
4 13507
 
0.9%
5 9411
 
0.6%
9 935
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 495490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1486470
75.0%
Latin 495490
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 941287
63.3%
1 154899
 
10.4%
8 151155
 
10.2%
2 70245
 
4.7%
7 66429
 
4.5%
6 60385
 
4.1%
3 18217
 
1.2%
4 13507
 
0.9%
5 9411
 
0.6%
9 935
 
0.1%
Latin
ValueCountFrequency (%)
P 495490
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1981960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 941287
47.5%
P 495490
25.0%
1 154899
 
7.8%
8 151155
 
7.6%
2 70245
 
3.5%
7 66429
 
3.4%
6 60385
 
3.0%
3 18217
 
0.9%
4 13507
 
0.7%
5 9411
 
0.5%

Tipo de movimento
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.9 MiB

Estoque especial
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing330355
Missing (%)64.0%
Memory size3.9 MiB
E
185032 
Q
 
1103

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters186135
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowE
5th rowE

Common Values

ValueCountFrequency (%)
E 185032
35.8%
Q 1103
 
0.2%
(Missing) 330355
64.0%

Length

2023-04-01T15:24:32.007828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:32.083891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
e 185032
99.4%
q 1103
 
0.6%

Most occurring characters

ValueCountFrequency (%)
E 185032
99.4%
Q 1103
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 186135
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 185032
99.4%
Q 1103
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 186135
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 185032
99.4%
Q 1103
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 185032
99.4%
Q 1103
 
0.6%

Doc.material
Real number (ℝ)

Distinct246943
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9059966 × 109
Minimum4.9 × 109
Maximum5.0000469 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:32.168175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.9 × 109
5-th percentile4.9000158 × 109
Q14.9001233 × 109
median4.9002775 × 109
Q34.900449 × 109
95-th percentile5.0000039 × 109
Maximum5.0000469 × 109
Range1.0004686 × 108
Interquartile range (IQR)325712.75

Descriptive statistics

Standard deviation23204531
Coefficient of variation (CV)0.0047298301
Kurtosis12.479007
Mean4.9059966 × 109
Median Absolute Deviation (MAD)162366
Skewness3.8049965
Sum2.5338982 × 1015
Variance5.3845024 × 1014
MonotonicityNot monotonic
2023-04-01T15:24:32.276877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4900000009 1695
 
0.3%
4900000228 1155
 
0.2%
4900000251 1155
 
0.2%
4900000003 1155
 
0.2%
4900000222 1155
 
0.2%
4900000226 1155
 
0.2%
4900000014 380
 
0.1%
4900112613 314
 
0.1%
4900112491 314
 
0.1%
4900141260 229
 
< 0.1%
Other values (246933) 507783
98.3%
ValueCountFrequency (%)
4900000003 1155
0.2%
4900000009 1695
0.3%
4900000012 106
 
< 0.1%
4900000013 21
 
< 0.1%
4900000014 380
 
0.1%
4900000016 2
 
< 0.1%
4900000017 2
 
< 0.1%
4900000018 2
 
< 0.1%
4900000019 2
 
< 0.1%
4900000022 2
 
< 0.1%
ValueCountFrequency (%)
5000046865 1
< 0.1%
5000046863 1
< 0.1%
5000046862 2
< 0.1%
5000046848 1
< 0.1%
5000046843 1
< 0.1%
5000046842 2
< 0.1%
5000046841 1
< 0.1%
5000046836 1
< 0.1%
5000046833 1
< 0.1%
5000046830 1
< 0.1%

Item doc.material
Real number (ℝ)

Distinct1695
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.957871
Minimum1
Maximum1695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:32.381141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile27
Maximum1695
Range1694
Interquartile range (IQR)1

Descriptive statistics

Standard deviation90.51207
Coefficient of variation (CV)6.4846614
Kurtosis130.62792
Mean13.957871
Median Absolute Deviation (MAD)1
Skewness10.807769
Sum7209101
Variance8192.4348
MonotonicityNot monotonic
2023-04-01T15:24:32.478366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 245501
47.5%
2 159829
30.9%
3 18239
 
3.5%
4 11469
 
2.2%
5 9668
 
1.9%
6 7732
 
1.5%
7 6367
 
1.2%
8 5126
 
1.0%
9 3522
 
0.7%
10 2617
 
0.5%
Other values (1685) 46420
 
9.0%
ValueCountFrequency (%)
1 245501
47.5%
2 159829
30.9%
3 18239
 
3.5%
4 11469
 
2.2%
5 9668
 
1.9%
6 7732
 
1.5%
7 6367
 
1.2%
8 5126
 
1.0%
9 3522
 
0.7%
10 2617
 
0.5%
ValueCountFrequency (%)
1695 1
< 0.1%
1694 1
< 0.1%
1693 1
< 0.1%
1692 1
< 0.1%
1691 1
< 0.1%
1690 1
< 0.1%
1689 1
< 0.1%
1688 1
< 0.1%
1687 1
< 0.1%
1686 1
< 0.1%
Distinct410
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
31/12/2021
 
8282
28/02/2023
 
3330
31/10/2022
 
3280
12/04/2022
 
3074
30/11/2022
 
3045
Other values (405)
495479 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5164900
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row24/08/2022
2nd row24/08/2022
3rd row24/08/2022
4th row24/08/2022
5th row23/08/2022

Common Values

ValueCountFrequency (%)
31/12/2021 8282
 
1.6%
28/02/2023 3330
 
0.6%
31/10/2022 3280
 
0.6%
12/04/2022 3074
 
0.6%
30/11/2022 3045
 
0.6%
27/10/2022 2917
 
0.6%
29/07/2022 2855
 
0.6%
16/02/2022 2663
 
0.5%
31/01/2022 2653
 
0.5%
29/03/2022 2633
 
0.5%
Other values (400) 481758
93.3%

Length

2023-04-01T15:24:32.569063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
31/12/2021 8282
 
1.6%
28/02/2023 3330
 
0.6%
31/10/2022 3280
 
0.6%
12/04/2022 3074
 
0.6%
30/11/2022 3045
 
0.6%
27/10/2022 2917
 
0.6%
29/07/2022 2855
 
0.6%
16/02/2022 2663
 
0.5%
31/01/2022 2653
 
0.5%
29/03/2022 2633
 
0.5%
Other values (400) 481758
93.3%

Most occurring characters

ValueCountFrequency (%)
2 1778174
34.4%
0 1149813
22.3%
/ 1032980
20.0%
1 452199
 
8.8%
3 221736
 
4.3%
8 95864
 
1.9%
7 93678
 
1.8%
9 92255
 
1.8%
6 86521
 
1.7%
5 81089
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4131920
80.0%
Other Punctuation 1032980
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1778174
43.0%
0 1149813
27.8%
1 452199
 
10.9%
3 221736
 
5.4%
8 95864
 
2.3%
7 93678
 
2.3%
9 92255
 
2.2%
6 86521
 
2.1%
5 81089
 
2.0%
4 80591
 
2.0%
Other Punctuation
ValueCountFrequency (%)
/ 1032980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5164900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1778174
34.4%
0 1149813
22.3%
/ 1032980
20.0%
1 452199
 
8.8%
3 221736
 
4.3%
8 95864
 
1.9%
7 93678
 
1.8%
9 92255
 
1.8%
6 86521
 
1.7%
5 81089
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5164900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1778174
34.4%
0 1149813
22.3%
/ 1032980
20.0%
1 452199
 
8.8%
3 221736
 
4.3%
8 95864
 
1.9%
7 93678
 
1.8%
9 92255
 
1.8%
6 86521
 
1.7%
5 81089
 
1.6%

Qtd. UM registro
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.9 MiB

UM registro
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
KG
455075 
UN
 
33182
M
 
10872
H
 
4374
PAR
 
4096
Other values (30)
 
8891

Length

Max length3
Median length2
Mean length1.9825108
Min length1

Characters and Unicode

Total characters1023947
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUA
2nd rowUA
3rd rowUA
4th rowUA
5th rowUA

Common Values

ValueCountFrequency (%)
KG 455075
88.1%
UN 33182
 
6.4%
M 10872
 
2.1%
H 4374
 
0.8%
PAR 4096
 
0.8%
HRS 2770
 
0.5%
L 1404
 
0.3%
PC 1053
 
0.2%
UA 869
 
0.2%
M3 637
 
0.1%
Other values (25) 2158
 
0.4%

Length

2023-04-01T15:24:32.645721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kg 455075
88.1%
un 33182
 
6.4%
m 10872
 
2.1%
h 4374
 
0.8%
par 4096
 
0.8%
hrs 2770
 
0.5%
l 1404
 
0.3%
pc 1053
 
0.2%
ua 869
 
0.2%
m3 637
 
0.1%
Other values (25) 2158
 
0.4%

Most occurring characters

ValueCountFrequency (%)
G 455400
44.5%
K 455075
44.4%
U 34051
 
3.3%
N 33186
 
3.2%
M 12037
 
1.2%
R 7500
 
0.7%
H 7148
 
0.7%
P 5193
 
0.5%
A 5010
 
0.5%
S 2796
 
0.3%
Other values (16) 6551
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1022700
99.9%
Decimal Number 987
 
0.1%
Lowercase Letter 260
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 455400
44.5%
K 455075
44.5%
U 34051
 
3.3%
N 33186
 
3.2%
M 12037
 
1.2%
R 7500
 
0.7%
H 7148
 
0.7%
P 5193
 
0.5%
A 5010
 
0.5%
S 2796
 
0.3%
Other values (12) 5304
 
0.5%
Decimal Number
ValueCountFrequency (%)
3 637
64.5%
2 350
35.5%
Lowercase Letter
ValueCountFrequency (%)
l 130
50.0%
t 130
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1022960
99.9%
Common 987
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 455400
44.5%
K 455075
44.5%
U 34051
 
3.3%
N 33186
 
3.2%
M 12037
 
1.2%
R 7500
 
0.7%
H 7148
 
0.7%
P 5193
 
0.5%
A 5010
 
0.5%
S 2796
 
0.3%
Other values (14) 5564
 
0.5%
Common
ValueCountFrequency (%)
3 637
64.5%
2 350
35.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1023947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 455400
44.5%
K 455075
44.4%
U 34051
 
3.3%
N 33186
 
3.2%
M 12037
 
1.2%
R 7500
 
0.7%
H 7148
 
0.7%
P 5193
 
0.5%
A 5010
 
0.5%
S 2796
 
0.3%
Other values (16) 6551
 
0.6%

Ano doc.material
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2022
431127 
2023
77081 
2021
 
8282

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2065960
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 431127
83.5%
2023 77081
 
14.9%
2021 8282
 
1.6%

Length

2023-04-01T15:24:32.746361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:32.819579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 431127
83.5%
2023 77081
 
14.9%
2021 8282
 
1.6%

Most occurring characters

ValueCountFrequency (%)
2 1464107
70.9%
0 516490
 
25.0%
3 77081
 
3.7%
1 8282
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2065960
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1464107
70.9%
0 516490
 
25.0%
3 77081
 
3.7%
1 8282
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2065960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1464107
70.9%
0 516490
 
25.0%
3 77081
 
3.7%
1 8282
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2065960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1464107
70.9%
0 516490
 
25.0%
3 77081
 
3.7%
1 8282
 
0.4%

Centro custo
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct78
Distinct (%)0.3%
Missing486352
Missing (%)94.2%
Memory size3.9 MiB
A001PLM501
4125 
A001PLM510
2791 
A001PLM511
2502 
A001PLM406
2155 
A001ALM301
1816 
Other values (73)
16749 

Length

Max length10
Median length10
Mean length9.8844316
Min length1

Characters and Unicode

Total characters297897
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowA001ALM301
2nd rowA001ALM301
3rd rowA001ALM301
4th rowA001ALM301
5th rowA001ALM301

Common Values

ValueCountFrequency (%)
A001PLM501 4125
 
0.8%
A001PLM510 2791
 
0.5%
A001PLM511 2502
 
0.5%
A001PLM406 2155
 
0.4%
A001ALM301 1816
 
0.4%
A001PLM512 1684
 
0.3%
A001PLM515 1560
 
0.3%
A001PLM504 1339
 
0.3%
A001PLM513 1280
 
0.2%
A001PLM514 817
 
0.2%
Other values (68) 10069
 
1.9%
(Missing) 486352
94.2%

Length

2023-04-01T15:24:32.885834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a001plm501 4125
13.7%
a001plm510 2791
 
9.3%
a001plm511 2502
 
8.3%
a001plm406 2155
 
7.2%
a001alm301 1816
 
6.0%
a001plm512 1684
 
5.6%
a001plm515 1560
 
5.2%
a001plm504 1339
 
4.4%
a001plm513 1280
 
4.2%
a001plm514 817
 
2.7%
Other values (68) 10069
33.4%

Most occurring characters

ValueCountFrequency (%)
0 75563
25.4%
1 55670
18.7%
A 34689
11.6%
L 28767
 
9.7%
M 28767
 
9.7%
P 24813
 
8.3%
5 23910
 
8.0%
4 6638
 
2.2%
2 6248
 
2.1%
3 3775
 
1.3%
Other values (14) 9057
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 178506
59.9%
Uppercase Letter 119004
39.9%
Other Punctuation 387
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 34689
29.1%
L 28767
24.2%
M 28767
24.2%
P 24813
20.9%
N 775
 
0.7%
G 775
 
0.7%
D 166
 
0.1%
U 131
 
0.1%
T 41
 
< 0.1%
R 41
 
< 0.1%
Other values (3) 39
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 75563
42.3%
1 55670
31.2%
5 23910
 
13.4%
4 6638
 
3.7%
2 6248
 
3.5%
3 3775
 
2.1%
6 3162
 
1.8%
9 2141
 
1.2%
8 739
 
0.4%
7 660
 
0.4%
Other Punctuation
ValueCountFrequency (%)
* 387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 178893
60.1%
Latin 119004
39.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 34689
29.1%
L 28767
24.2%
M 28767
24.2%
P 24813
20.9%
N 775
 
0.7%
G 775
 
0.7%
D 166
 
0.1%
U 131
 
0.1%
T 41
 
< 0.1%
R 41
 
< 0.1%
Other values (3) 39
 
< 0.1%
Common
ValueCountFrequency (%)
0 75563
42.2%
1 55670
31.1%
5 23910
 
13.4%
4 6638
 
3.7%
2 6248
 
3.5%
3 3775
 
2.1%
6 3162
 
1.8%
9 2141
 
1.2%
8 739
 
0.4%
7 660
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 297897
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 75563
25.4%
1 55670
18.7%
A 34689
11.6%
L 28767
 
9.7%
M 28767
 
9.7%
P 24813
 
8.3%
5 23910
 
8.0%
4 6638
 
2.2%
2 6248
 
2.1%
3 3775
 
1.3%
Other values (14) 9057
 
3.0%

Classificação contábil múltipla
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing516103
Missing (%)99.9%
Memory size3.9 MiB
X
387 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters387
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
3rd rowX
4th rowX
5th rowX

Common Values

ValueCountFrequency (%)
X 387
 
0.1%
(Missing) 516103
99.9%

Length

2023-04-01T15:24:32.954490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:33.022920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
x 387
100.0%

Most occurring characters

ValueCountFrequency (%)
X 387
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 387
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 387
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 387
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
X 387
100.0%

Cliente
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct340
Distinct (%)1.1%
Missing486079
Missing (%)94.1%
Infinite0
Infinite (%)0.0%
Mean1007460.9
Minimum1000045
Maximum1019663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:33.097825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1000045
5-th percentile1000318
Q11003116
median1007400
Q31009676
95-th percentile1017571
Maximum1019663
Range19618
Interquartile range (IQR)6560

Descriptive statistics

Standard deviation5009.1543
Coefficient of variation (CV)0.0049720581
Kurtosis-0.2886062
Mean1007460.9
Median Absolute Deviation (MAD)3102
Skewness0.47594076
Sum3.0637895 × 1010
Variance25091627
MonotonicityNot monotonic
2023-04-01T15:24:33.206773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1003116 3045
 
0.6%
1000319 2509
 
0.5%
1009610 2164
 
0.4%
1010502 1341
 
0.3%
1007101 1322
 
0.3%
1016832 1316
 
0.3%
1007984 1260
 
0.2%
1003827 1207
 
0.2%
1007400 1109
 
0.2%
1007247 885
 
0.2%
Other values (330) 14253
 
2.8%
(Missing) 486079
94.1%
ValueCountFrequency (%)
1000045 5
 
< 0.1%
1000068 46
 
< 0.1%
1000077 1
 
< 0.1%
1000112 1
 
< 0.1%
1000140 37
 
< 0.1%
1000225 3
 
< 0.1%
1000302 857
0.2%
1000304 35
 
< 0.1%
1000306 26
 
< 0.1%
1000317 207
 
< 0.1%
ValueCountFrequency (%)
1019663 3
 
< 0.1%
1019652 7
 
< 0.1%
1019540 1
 
< 0.1%
1019531 1
 
< 0.1%
1019502 160
< 0.1%
1019495 3
 
< 0.1%
1019450 2
 
< 0.1%
1019440 1
 
< 0.1%
1019420 3
 
< 0.1%
1019380 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
H
272739 
S
243751 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters516490
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
H 272739
52.8%
S 243751
47.2%

Length

2023-04-01T15:24:33.303324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:33.373400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
h 272739
52.8%
s 243751
47.2%

Most occurring characters

ValueCountFrequency (%)
H 272739
52.8%
S 243751
47.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 516490
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 272739
52.8%
S 243751
47.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 516490
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 272739
52.8%
S 243751
47.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 516490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 272739
52.8%
S 243751
47.2%

Código de movimento
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing389045
Missing (%)75.3%
Memory size3.9 MiB
F
54483 
L
43312 
B
29650 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters127445
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
F 54483
 
10.5%
L 43312
 
8.4%
B 29650
 
5.7%
(Missing) 389045
75.3%

Length

2023-04-01T15:24:33.436327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:33.515289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
f 54483
42.8%
l 43312
34.0%
b 29650
23.3%

Most occurring characters

ValueCountFrequency (%)
F 54483
42.8%
L 43312
34.0%
B 29650
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 127445
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 54483
42.8%
L 43312
34.0%
B 29650
23.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 127445
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 54483
42.8%
L 43312
34.0%
B 29650
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 54483
42.8%
L 43312
34.0%
B 29650
23.3%

Código entrada
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing496147
Missing (%)96.1%
Memory size3.9 MiB
X
20343 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20343
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
3rd rowX
4th rowX
5th rowX

Common Values

ValueCountFrequency (%)
X 20343
 
3.9%
(Missing) 496147
96.1%

Length

2023-04-01T15:24:33.583332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:33.659699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
x 20343
100.0%

Most occurring characters

ValueCountFrequency (%)
X 20343
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 20343
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 20343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20343
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 20343
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
X 20343
100.0%

Consumo
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing500295
Missing (%)96.9%
Memory size3.9 MiB
V
15113 
P
 
960
A
 
81
E
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16195
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV
2nd rowV
3rd rowV
4th rowV
5th rowV

Common Values

ValueCountFrequency (%)
V 15113
 
2.9%
P 960
 
0.2%
A 81
 
< 0.1%
E 41
 
< 0.1%
(Missing) 500295
96.9%

Length

2023-04-01T15:24:33.726822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:33.812909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
v 15113
93.3%
p 960
 
5.9%
a 81
 
0.5%
e 41
 
0.3%

Most occurring characters

ValueCountFrequency (%)
V 15113
93.3%
P 960
 
5.9%
A 81
 
0.5%
E 41
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 16195
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
V 15113
93.3%
P 960
 
5.9%
A 81
 
0.5%
E 41
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 16195
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
V 15113
93.3%
P 960
 
5.9%
A 81
 
0.5%
E 41
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
V 15113
93.3%
P 960
 
5.9%
A 81
 
0.5%
E 41
 
0.3%

Contador
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88611783
Minimum0
Maximum18
Zeros250126
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:33.878839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4563623
Coefficient of variation (CV)1.6435312
Kurtosis13.037065
Mean0.88611783
Median Absolute Deviation (MAD)1
Skewness3.1888112
Sum457671
Variance2.1209911
MonotonicityNot monotonic
2023-04-01T15:24:33.946486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 250126
48.4%
1 202865
39.3%
3 19671
 
3.8%
2 16228
 
3.1%
4 8488
 
1.6%
6 7949
 
1.5%
8 3135
 
0.6%
5 2952
 
0.6%
7 2712
 
0.5%
9 1838
 
0.4%
Other values (7) 526
 
0.1%
ValueCountFrequency (%)
0 250126
48.4%
1 202865
39.3%
2 16228
 
3.1%
3 19671
 
3.8%
4 8488
 
1.6%
5 2952
 
0.6%
6 7949
 
1.5%
7 2712
 
0.5%
8 3135
 
0.6%
9 1838
 
0.4%
ValueCountFrequency (%)
18 22
 
< 0.1%
16 8
 
< 0.1%
15 43
 
< 0.1%
14 3
 
< 0.1%
13 26
 
< 0.1%
12 268
 
0.1%
10 156
 
< 0.1%
9 1838
0.4%
8 3135
0.6%
7 2712
0.5%

Data de entrada
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.9 MiB

Data do documento
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.9 MiB

Diagrama de rede
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing510921
Missing (%)98.9%
Memory size3.9 MiB
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0
516490 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters516490
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 516490
100.0%

Length

2023-04-01T15:24:34.042849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:34.111438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 516490
100.0%

Most occurring characters

ValueCountFrequency (%)
0 516490
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 516490
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 516490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 516490
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 516490
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 516490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 516490
100.0%

Documento do depósito
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing516490
Missing (%)100.0%
Memory size3.9 MiB

Elemento PEP
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)6.9%
Missing515621
Missing (%)99.8%
Memory size3.9 MiB
I.0000000004.02.02.01
98 
I.0000000004.02.01
97 
I.0000000011.01.03
59 
I.0000000003.01.02.01
52 
I.0000000003.03.02
 
49
Other values (55)
514 

Length

Max length21
Median length18
Mean length19.266974
Min length15

Characters and Unicode

Total characters16743
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)1.2%

Sample

1st rowI.0000000004.02.01.01
2nd rowI.0000000004.02.01
3rd rowI.0000000004.02.01.01
4th rowI.0000000004.02.01.01
5th rowI.0000000004.02.01.01

Common Values

ValueCountFrequency (%)
I.0000000004.02.02.01 98
 
< 0.1%
I.0000000004.02.01 97
 
< 0.1%
I.0000000011.01.03 59
 
< 0.1%
I.0000000003.01.02.01 52
 
< 0.1%
I.0000000003.03.02 49
 
< 0.1%
I.0000000004.02.03 44
 
< 0.1%
I.0000000011.01.02 42
 
< 0.1%
I.0000000017.01.04 37
 
< 0.1%
I.0000000004.02.02.05 34
 
< 0.1%
I.0000000003.01.02.08 29
 
< 0.1%
Other values (50) 328
 
0.1%
(Missing) 515621
99.8%

Length

2023-04-01T15:24:34.171034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i.0000000004.02.02.01 98
 
11.3%
i.0000000004.02.01 97
 
11.2%
i.0000000011.01.03 59
 
6.8%
i.0000000003.01.02.01 52
 
6.0%
i.0000000003.03.02 49
 
5.6%
i.0000000004.02.03 44
 
5.1%
i.0000000011.01.02 42
 
4.8%
i.0000000017.01.04 37
 
4.3%
i.0000000004.02.02.05 34
 
3.9%
i.0000000003.01.02.08 29
 
3.3%
Other values (50) 328
37.7%

Most occurring characters

ValueCountFrequency (%)
0 9698
57.9%
. 2974
 
17.8%
1 1102
 
6.6%
I 869
 
5.2%
2 848
 
5.1%
4 572
 
3.4%
3 463
 
2.8%
7 89
 
0.5%
8 64
 
0.4%
5 50
 
0.3%
Other values (2) 14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12900
77.0%
Other Punctuation 2974
 
17.8%
Uppercase Letter 869
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9698
75.2%
1 1102
 
8.5%
2 848
 
6.6%
4 572
 
4.4%
3 463
 
3.6%
7 89
 
0.7%
8 64
 
0.5%
5 50
 
0.4%
6 9
 
0.1%
9 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 2974
100.0%
Uppercase Letter
ValueCountFrequency (%)
I 869
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15874
94.8%
Latin 869
 
5.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9698
61.1%
. 2974
 
18.7%
1 1102
 
6.9%
2 848
 
5.3%
4 572
 
3.6%
3 463
 
2.9%
7 89
 
0.6%
8 64
 
0.4%
5 50
 
0.3%
6 9
 
0.1%
Latin
ValueCountFrequency (%)
I 869
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16743
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9698
57.9%
. 2974
 
17.8%
1 1102
 
6.6%
I 869
 
5.2%
2 848
 
5.1%
4 572
 
3.4%
3 463
 
2.8%
7 89
 
0.5%
8 64
 
0.4%
5 50
 
0.3%
Other values (2) 14
 
0.1%

Empresa
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
1000
516490 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2065960
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000
2nd row1000
3rd row1000
4th row1000
5th row1000

Common Values

ValueCountFrequency (%)
1000 516490
100.0%

Length

2023-04-01T15:24:34.243451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:34.310971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1000 516490
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1549470
75.0%
1 516490
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2065960
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1549470
75.0%
1 516490
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2065960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1549470
75.0%
1 516490
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2065960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1549470
75.0%
1 516490
 
25.0%

Fornecedor
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing479220
Missing (%)92.8%
Memory size3.9 MiB

Hora do registro
Categorical

Distinct92767
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0,839178241
 
1695
0,373993056
 
1271
0,408425926
 
1170
0,622314815
 
1163
0,43287037
 
1161
Other values (92762)
510030 

Length

Max length11
Median length10
Mean length9.507458
Min length3

Characters and Unicode

Total characters4910507
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12147 ?
Unique (%)2.4%

Sample

1st row08:21:57
2nd row09:27:09
3rd row08:27:52
4th row08:25:24
5th row17:50:31

Common Values

ValueCountFrequency (%)
0,839178241 1695
 
0.3%
0,373993056 1271
 
0.2%
0,408425926 1170
 
0.2%
0,622314815 1163
 
0.2%
0,43287037 1161
 
0.2%
0,392407407 1158
 
0.2%
0,800173611 380
 
0.1%
0,379733796 322
 
0.1%
0,397893519 316
 
0.1%
0,440347222 235
 
< 0.1%
Other values (92757) 507619
98.3%

Length

2023-04-01T15:24:34.382727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,839178241 1695
 
0.3%
0,373993056 1271
 
0.2%
0,408425926 1170
 
0.2%
0,622314815 1163
 
0.2%
0,43287037 1161
 
0.2%
0,392407407 1158
 
0.2%
0,800173611 380
 
0.1%
0,379733796 322
 
0.1%
0,397893519 316
 
0.1%
0,440347222 235
 
< 0.1%
Other values (92757) 507619
98.3%

Most occurring characters

ValueCountFrequency (%)
0 678857
13.8%
1 554204
11.3%
: 480374
9.8%
4 452343
9.2%
3 413623
8.4%
5 404878
8.2%
2 362179
7.4%
6 352821
7.2%
7 350083
7.1%
8 293007
6.0%
Other values (4) 568138
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4153818
84.6%
Other Punctuation 756677
 
15.4%
Uppercase Letter 6
 
< 0.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 678857
16.3%
1 554204
13.3%
4 452343
10.9%
3 413623
10.0%
5 404878
9.7%
2 362179
8.7%
6 352821
8.5%
7 350083
8.4%
8 293007
7.1%
9 291823
7.0%
Other Punctuation
ValueCountFrequency (%)
: 480374
63.5%
, 276303
36.5%
Uppercase Letter
ValueCountFrequency (%)
E 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4910501
> 99.9%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 678857
13.8%
1 554204
11.3%
: 480374
9.8%
4 452343
9.2%
3 413623
8.4%
5 404878
8.2%
2 362179
7.4%
6 352821
7.2%
7 350083
7.1%
8 293007
6.0%
Other values (3) 568132
11.6%
Latin
ValueCountFrequency (%)
E 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4910507
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 678857
13.8%
1 554204
11.3%
: 480374
9.8%
4 452343
9.2%
3 413623
8.4%
5 404878
8.2%
2 362179
7.4%
6 352821
7.2%
7 350083
7.1%
8 293007
6.0%
Other values (4) 568138
11.6%

Imobilizado
Categorical

HIGH CARDINALITY  HIGH CORRELATION  IMBALANCE  MISSING 

Distinct56
Distinct (%)12.5%
Missing516042
Missing (%)99.9%
Memory size3.9 MiB
*
387 
20001435
 
3
30003568
 
3
30003567
 
3
20001659
 
1
Other values (51)
51 

Length

Max length8
Median length1
Mean length1.953125
Min length1

Characters and Unicode

Total characters875
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)11.6%

Sample

1st row*
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 387
 
0.1%
20001435 3
 
< 0.1%
30003568 3
 
< 0.1%
30003567 3
 
< 0.1%
20001659 1
 
< 0.1%
20001405 1
 
< 0.1%
20001444 1
 
< 0.1%
20001466 1
 
< 0.1%
20001420 1
 
< 0.1%
20001429 1
 
< 0.1%
Other values (46) 46
 
< 0.1%
(Missing) 516042
99.9%

Length

2023-04-01T15:24:34.463795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
387
86.4%
30003567 3
 
0.7%
20001435 3
 
0.7%
30003568 3
 
0.7%
20001951 1
 
0.2%
20001891 1
 
0.2%
20001811 1
 
0.2%
20001961 1
 
0.2%
20001937 1
 
0.2%
20001810 1
 
0.2%
Other values (46) 46
 
10.3%

Most occurring characters

ValueCountFrequency (%)
* 387
44.2%
0 198
22.6%
2 72
 
8.2%
1 58
 
6.6%
4 27
 
3.1%
5 27
 
3.1%
3 26
 
3.0%
8 25
 
2.9%
6 22
 
2.5%
9 22
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 488
55.8%
Other Punctuation 387
44.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 198
40.6%
2 72
 
14.8%
1 58
 
11.9%
4 27
 
5.5%
5 27
 
5.5%
3 26
 
5.3%
8 25
 
5.1%
6 22
 
4.5%
9 22
 
4.5%
7 11
 
2.3%
Other Punctuation
ValueCountFrequency (%)
* 387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 875
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 387
44.2%
0 198
22.6%
2 72
 
8.2%
1 58
 
6.6%
4 27
 
3.1%
5 27
 
3.1%
3 26
 
3.0%
8 25
 
2.9%
6 22
 
2.5%
9 22
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 387
44.2%
0 198
22.6%
2 72
 
8.2%
1 58
 
6.6%
4 27
 
3.1%
5 27
 
3.1%
3 26
 
3.0%
8 25
 
2.9%
6 22
 
2.5%
9 22
 
2.5%

Item
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.731263
Minimum0
Maximum2600
Zeros465255
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:34.547749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile60
Maximum2600
Range2600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation70.049757
Coefficient of variation (CV)5.1014796
Kurtosis62.930207
Mean13.731263
Median Absolute Deviation (MAD)0
Skewness7.2146274
Sum7092060
Variance4906.9685
MonotonicityNot monotonic
2023-04-01T15:24:34.645442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 465255
90.1%
10 13986
 
2.7%
20 4395
 
0.9%
30 2740
 
0.5%
40 2164
 
0.4%
50 1785
 
0.3%
60 1572
 
0.3%
70 1387
 
0.3%
80 1282
 
0.2%
90 1160
 
0.2%
Other values (101) 20764
 
4.0%
ValueCountFrequency (%)
0 465255
90.1%
10 13986
 
2.7%
20 4395
 
0.9%
30 2740
 
0.5%
40 2164
 
0.4%
50 1785
 
0.3%
60 1572
 
0.3%
70 1387
 
0.3%
80 1282
 
0.2%
90 1160
 
0.2%
ValueCountFrequency (%)
2600 1
 
< 0.1%
1090 4
< 0.1%
1080 4
< 0.1%
1070 4
< 0.1%
1060 8
< 0.1%
1050 8
< 0.1%
1040 8
< 0.1%
1030 8
< 0.1%
1020 8
< 0.1%
1010 8
< 0.1%

Item gerado automaticamente
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing403344
Missing (%)78.1%
Memory size3.9 MiB
X
113146 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113146
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowX
2nd rowX
3rd rowX
4th rowX
5th rowX

Common Values

ValueCountFrequency (%)
X 113146
 
21.9%
(Missing) 403344
78.1%

Length

2023-04-01T15:24:34.731246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:34.798792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
x 113146
100.0%

Most occurring characters

ValueCountFrequency (%)
X 113146
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 113146
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 113146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 113146
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 113146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
X 113146
100.0%

Item ord.cliente
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.208873
Minimum0
Maximum500
Zeros359865
Zeros (%)69.7%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:34.875141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310
95-th percentile60
Maximum500
Range500
Interquartile range (IQR)10

Descriptive statistics

Standard deviation25.534336
Coefficient of variation (CV)2.5011904
Kurtosis38.601073
Mean10.208873
Median Absolute Deviation (MAD)0
Skewness4.884531
Sum5272781
Variance652.00233
MonotonicityNot monotonic
2023-04-01T15:24:34.986523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 359865
69.7%
10 64360
 
12.5%
20 27011
 
5.2%
30 17129
 
3.3%
40 11665
 
2.3%
50 8545
 
1.7%
60 5959
 
1.2%
70 5358
 
1.0%
80 3722
 
0.7%
90 3103
 
0.6%
Other values (49) 9773
 
1.9%
ValueCountFrequency (%)
0 359865
69.7%
1 15
 
< 0.1%
10 64360
 
12.5%
11 10
 
< 0.1%
20 27011
 
5.2%
21 6
 
< 0.1%
30 17129
 
3.3%
31 12
 
< 0.1%
36 6
 
< 0.1%
40 11665
 
2.3%
ValueCountFrequency (%)
500 5
< 0.1%
490 7
< 0.1%
480 5
< 0.1%
470 10
< 0.1%
460 7
< 0.1%
450 5
< 0.1%
440 5
< 0.1%
420 4
 
< 0.1%
410 6
< 0.1%
400 7
< 0.1%

Item ord.cliente.1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.90988
Minimum0
Maximum500
Zeros331458
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:35.089393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310
95-th percentile60
Maximum500
Range500
Interquartile range (IQR)10

Descriptive statistics

Standard deviation27.00965
Coefficient of variation (CV)2.2678356
Kurtosis33.909088
Mean11.90988
Median Absolute Deviation (MAD)0
Skewness4.5396524
Sum6151334
Variance729.5212
MonotonicityNot monotonic
2023-04-01T15:24:35.188589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 331458
64.2%
10 76961
 
14.9%
20 31785
 
6.2%
30 20152
 
3.9%
40 13800
 
2.7%
50 9997
 
1.9%
60 7006
 
1.4%
70 6244
 
1.2%
80 4324
 
0.8%
90 3625
 
0.7%
Other values (49) 11138
 
2.2%
ValueCountFrequency (%)
0 331458
64.2%
1 18
 
< 0.1%
10 76961
 
14.9%
11 12
 
< 0.1%
20 31785
 
6.2%
21 8
 
< 0.1%
30 20152
 
3.9%
31 16
 
< 0.1%
36 8
 
< 0.1%
40 13800
 
2.7%
ValueCountFrequency (%)
500 6
< 0.1%
490 8
< 0.1%
480 6
< 0.1%
470 12
< 0.1%
460 8
< 0.1%
450 6
< 0.1%
440 6
< 0.1%
420 4
 
< 0.1%
410 6
< 0.1%
400 8
< 0.1%

Linha original
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0014172588
Minimum0
Maximum46
Zeros516103
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:35.271583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum46
Range46
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.12159894
Coefficient of variation (CV)85.798684
Kurtosis66417.359
Mean0.0014172588
Median Absolute Deviation (MAD)0
Skewness227.21922
Sum732
Variance0.014786302
MonotonicityNot monotonic
2023-04-01T15:24:35.343173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 516103
99.9%
1 351
 
0.1%
5 7
 
< 0.1%
4 4
 
< 0.1%
3 3
 
< 0.1%
7 3
 
< 0.1%
12 3
 
< 0.1%
8 2
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
Other values (10) 10
 
< 0.1%
ValueCountFrequency (%)
0 516103
99.9%
1 351
 
0.1%
2 1
 
< 0.1%
3 3
 
< 0.1%
4 4
 
< 0.1%
5 7
 
< 0.1%
6 1
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
46 1
 
< 0.1%
35 1
 
< 0.1%
29 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
14 2
< 0.1%
13 2
< 0.1%
12 3
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%

Lote
Categorical

HIGH CARDINALITY  MISSING 

Distinct50684
Distinct (%)14.3%
Missing162644
Missing (%)31.5%
Memory size3.9 MiB
22B0675003
 
94
22B0050001
 
80
22A0025001
 
78
21B0479001
 
74
22A0175001
 
69
Other values (50679)
353451 

Length

Max length10
Median length10
Mean length9.8404701
Min length3

Characters and Unicode

Total characters3482011
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)< 0.1%

Sample

1st row23B0001
2nd row23B0001
3rd row23A0011
4th row23A0011
5th row23A0011

Common Values

ValueCountFrequency (%)
22B0675003 94
 
< 0.1%
22B0050001 80
 
< 0.1%
22A0025001 78
 
< 0.1%
21B0479001 74
 
< 0.1%
22A0175001 69
 
< 0.1%
22A0222001 69
 
< 0.1%
22B0209001 61
 
< 0.1%
22B0479001 60
 
< 0.1%
22C0660001 57
 
< 0.1%
22B0789001 56
 
< 0.1%
Other values (50674) 353148
68.4%
(Missing) 162644
31.5%

Length

2023-04-01T15:24:35.427288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
22b0675003 94
 
< 0.1%
22b0050001 80
 
< 0.1%
22a0025001 78
 
< 0.1%
21b0479001 74
 
< 0.1%
22a0175001 69
 
< 0.1%
22a0222001 69
 
< 0.1%
22b0209001 61
 
< 0.1%
22b0479001 60
 
< 0.1%
22c0660001 57
 
< 0.1%
22b0789001 56
 
< 0.1%
Other values (50675) 353152
99.8%

Most occurring characters

ValueCountFrequency (%)
0 1020480
29.3%
2 811422
23.3%
1 337179
 
9.7%
3 187401
 
5.4%
B 177269
 
5.1%
4 153743
 
4.4%
5 140309
 
4.0%
6 129077
 
3.7%
7 120369
 
3.5%
8 115561
 
3.3%
Other values (18) 289201
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3122086
89.7%
Uppercase Letter 359913
 
10.3%
Other Punctuation 8
 
< 0.1%
Space Separator 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 177269
49.3%
C 106594
29.6%
A 75333
20.9%
D 190
 
0.1%
E 146
 
< 0.1%
F 131
 
< 0.1%
H 97
 
< 0.1%
I 78
 
< 0.1%
G 33
 
< 0.1%
S 12
 
< 0.1%
Other values (5) 30
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 1020480
32.7%
2 811422
26.0%
1 337179
 
10.8%
3 187401
 
6.0%
4 153743
 
4.9%
5 140309
 
4.5%
6 129077
 
4.1%
7 120369
 
3.9%
8 115561
 
3.7%
9 106545
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/ 4
50.0%
, 4
50.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3122098
89.7%
Latin 359913
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 177269
49.3%
C 106594
29.6%
A 75333
20.9%
D 190
 
0.1%
E 146
 
< 0.1%
F 131
 
< 0.1%
H 97
 
< 0.1%
I 78
 
< 0.1%
G 33
 
< 0.1%
S 12
 
< 0.1%
Other values (5) 30
 
< 0.1%
Common
ValueCountFrequency (%)
0 1020480
32.7%
2 811422
26.0%
1 337179
 
10.8%
3 187401
 
6.0%
4 153743
 
4.9%
5 140309
 
4.5%
6 129077
 
4.1%
7 120369
 
3.9%
8 115561
 
3.7%
9 106545
 
3.4%
Other values (3) 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3482011
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1020480
29.3%
2 811422
23.3%
1 337179
 
9.7%
3 187401
 
5.4%
B 177269
 
5.1%
4 153743
 
4.4%
5 140309
 
4.0%
6 129077
 
3.7%
7 120369
 
3.5%
8 115561
 
3.3%
Other values (18) 289201
 
8.3%

Material Alterado
Real number (ℝ)

Distinct7781
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3046.7611
Minimum0
Maximum9817
Zeros1655
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:35.518872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2032
Q12256
median2911
Q33404
95-th percentile4786
Maximum9817
Range9817
Interquartile range (IQR)1148

Descriptive statistics

Standard deviation1113.8156
Coefficient of variation (CV)0.36557366
Kurtosis10.791163
Mean3046.7611
Median Absolute Deviation (MAD)520
Skewness2.6938641
Sum1.5736216 × 109
Variance1240585.1
MonotonicityNot monotonic
2023-04-01T15:24:35.617836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2087 39672
 
7.7%
3409 21385
 
4.1%
2886 11420
 
2.2%
2628 8559
 
1.7%
3357 7806
 
1.5%
3444 6888
 
1.3%
2461 6286
 
1.2%
2086 5690
 
1.1%
3321 5313
 
1.0%
2303 5128
 
1.0%
Other values (7771) 398343
77.1%
ValueCountFrequency (%)
0 1655
 
0.3%
2019 2
 
< 0.1%
2020 3320
0.6%
2021 689
 
0.1%
2022 3662
0.7%
2023 1983
0.4%
2024 3014
0.6%
2025 4460
0.9%
2026 642
 
0.1%
2027 245
 
< 0.1%
ValueCountFrequency (%)
9817 2
 
< 0.1%
9816 15
< 0.1%
9815 1
 
< 0.1%
9814 7
< 0.1%
9813 1
 
< 0.1%
9812 5
 
< 0.1%
9811 5
 
< 0.1%
9810 1
 
< 0.1%
9809 3
 
< 0.1%
9808 5
 
< 0.1%

Classificação
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
Acabado
199063 
Semi-acabado
159029 
Materia prima
99952 
Insumo 5
25450 
Insumo 4
 
15722
Other values (5)
 
17274

Length

Max length13
Median length12
Mean length9.7945807
Min length2

Characters and Unicode

Total characters5058803
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row##
2nd row##
3rd row##
4th row##
5th row##

Common Values

ValueCountFrequency (%)
Acabado 199063
38.5%
Semi-acabado 159029
30.8%
Materia prima 99952
19.4%
Insumo 5 25450
 
4.9%
Insumo 4 15722
 
3.0%
Insumo 2 10590
 
2.1%
Insumo 3 4041
 
0.8%
## 1655
 
0.3%
Insumo 1 596
 
0.1%
Insumo 6 392
 
0.1%

Length

2023-04-01T15:24:35.706818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:35.794942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
acabado 199063
29.6%
semi-acabado 159029
23.6%
materia 99952
14.8%
prima 99952
14.8%
insumo 56791
 
8.4%
5 25450
 
3.8%
4 15722
 
2.3%
2 10590
 
1.6%
3 4041
 
0.6%
1655
 
0.2%
Other values (2) 988
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1175069
23.2%
o 414883
 
8.2%
i 358933
 
7.1%
b 358092
 
7.1%
d 358092
 
7.1%
c 358092
 
7.1%
m 315772
 
6.2%
e 258981
 
5.1%
r 199904
 
4.0%
A 199063
 
3.9%
Other values (17) 1061922
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4168095
82.4%
Uppercase Letter 514835
 
10.2%
Dash Punctuation 159029
 
3.1%
Space Separator 156743
 
3.1%
Decimal Number 56791
 
1.1%
Other Punctuation 3310
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1175069
28.2%
o 414883
 
10.0%
i 358933
 
8.6%
b 358092
 
8.6%
d 358092
 
8.6%
c 358092
 
8.6%
m 315772
 
7.6%
e 258981
 
6.2%
r 199904
 
4.8%
t 99952
 
2.4%
Other values (4) 270325
 
6.5%
Decimal Number
ValueCountFrequency (%)
5 25450
44.8%
4 15722
27.7%
2 10590
18.6%
3 4041
 
7.1%
1 596
 
1.0%
6 392
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
A 199063
38.7%
S 159029
30.9%
M 99952
19.4%
I 56791
 
11.0%
Dash Punctuation
ValueCountFrequency (%)
- 159029
100.0%
Space Separator
ValueCountFrequency (%)
156743
100.0%
Other Punctuation
ValueCountFrequency (%)
# 3310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4682930
92.6%
Common 375873
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1175069
25.1%
o 414883
 
8.9%
i 358933
 
7.7%
b 358092
 
7.6%
d 358092
 
7.6%
c 358092
 
7.6%
m 315772
 
6.7%
e 258981
 
5.5%
r 199904
 
4.3%
A 199063
 
4.3%
Other values (8) 686049
14.6%
Common
ValueCountFrequency (%)
- 159029
42.3%
156743
41.7%
5 25450
 
6.8%
4 15722
 
4.2%
2 10590
 
2.8%
3 4041
 
1.1%
# 3310
 
0.9%
1 596
 
0.2%
6 392
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5058803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1175069
23.2%
o 414883
 
8.2%
i 358933
 
7.1%
b 358092
 
7.1%
d 358092
 
7.1%
c 358092
 
7.1%
m 315772
 
6.2%
e 258981
 
5.1%
r 199904
 
4.0%
A 199063
 
3.9%
Other values (17) 1061922
21.0%

Moeda
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
BRL
516490 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1549470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBRL
2nd rowBRL
3rd rowBRL
4th rowBRL
5th rowBRL

Common Values

ValueCountFrequency (%)
BRL 516490
100.0%

Length

2023-04-01T15:24:35.885549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:35.953452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
brl 516490
100.0%

Most occurring characters

ValueCountFrequency (%)
B 516490
33.3%
R 516490
33.3%
L 516490
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1549470
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 516490
33.3%
R 516490
33.3%
L 516490
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1549470
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 516490
33.3%
R 516490
33.3%
L 516490
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1549470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 516490
33.3%
R 516490
33.3%
L 516490
33.3%

Montante em MI
Categorical

Distinct156915
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
0
115981 
0,00
105388 
39,24
 
1008
26,16
 
747
52,32
 
668
Other values (156910)
292698 

Length

Max length13
Median length11
Mean length5.2157505
Min length1

Characters and Unicode

Total characters2693883
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112298 ?
Unique (%)21.7%

Sample

1st row7.156,97
2nd row7.156,83
3rd row7.156,89
4th row7.156,74
5th row7.157,45

Common Values

ValueCountFrequency (%)
0 115981
 
22.5%
0,00 105388
 
20.4%
39,24 1008
 
0.2%
26,16 747
 
0.1%
52,32 668
 
0.1%
2616 444
 
0.1%
104,64 398
 
0.1%
78,48 386
 
0.1%
130,8 355
 
0.1%
271,37 352
 
0.1%
Other values (156905) 290763
56.3%

Length

2023-04-01T15:24:36.028013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 115981
 
22.5%
0,00 105388
 
20.4%
39,24 1045
 
0.2%
26,16 776
 
0.2%
52,32 712
 
0.1%
271,37 662
 
0.1%
2616 462
 
0.1%
104,64 425
 
0.1%
78,48 413
 
0.1%
130,8 387
 
0.1%
Other values (115332) 290239
56.2%

Most occurring characters

ValueCountFrequency (%)
0 600574
22.3%
, 380034
14.1%
1 213704
 
7.9%
2 200082
 
7.4%
4 168465
 
6.3%
- 164527
 
6.1%
6 162602
 
6.0%
8 155659
 
5.8%
3 152055
 
5.6%
5 148610
 
5.5%
Other values (3) 347571
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2065050
76.7%
Other Punctuation 464306
 
17.2%
Dash Punctuation 164527
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 600574
29.1%
1 213704
 
10.3%
2 200082
 
9.7%
4 168465
 
8.2%
6 162602
 
7.9%
8 155659
 
7.5%
3 152055
 
7.4%
5 148610
 
7.2%
7 134167
 
6.5%
9 129132
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 380034
81.8%
. 84272
 
18.2%
Dash Punctuation
ValueCountFrequency (%)
- 164527
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2693883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 600574
22.3%
, 380034
14.1%
1 213704
 
7.9%
2 200082
 
7.4%
4 168465
 
6.3%
- 164527
 
6.1%
6 162602
 
6.0%
8 155659
 
5.8%
3 152055
 
5.6%
5 148610
 
5.5%
Other values (3) 347571
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2693883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 600574
22.3%
, 380034
14.1%
1 213704
 
7.9%
2 200082
 
7.4%
4 168465
 
6.3%
- 164527
 
6.1%
6 162602
 
6.0%
8 155659
 
5.8%
3 152055
 
5.6%
5 148610
 
5.5%
Other values (3) 347571
12.9%

Montante externo em MI
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.9 MiB

Motivo do movimento
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014464946
Minimum0
Maximum203
Zeros516202
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:36.108261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum203
Range203
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.114841
Coefficient of variation (CV)77.071909
Kurtosis22747.429
Mean0.014464946
Median Absolute Deviation (MAD)0
Skewness139.65065
Sum7471
Variance1.2428705
MonotonicityNot monotonic
2023-04-01T15:24:36.184872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 516202
99.9%
4 46
 
< 0.1%
25 45
 
< 0.1%
24 42
 
< 0.1%
27 40
 
< 0.1%
7 27
 
< 0.1%
12 25
 
< 0.1%
2 16
 
< 0.1%
1 15
 
< 0.1%
193 10
 
< 0.1%
Other values (15) 22
 
< 0.1%
ValueCountFrequency (%)
0 516202
99.9%
1 15
 
< 0.1%
2 16
 
< 0.1%
3 1
 
< 0.1%
4 46
 
< 0.1%
7 27
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
12 25
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
203 1
 
< 0.1%
193 10
< 0.1%
187 1
 
< 0.1%
152 1
 
< 0.1%
130 2
 
< 0.1%
73 1
 
< 0.1%
71 1
 
< 0.1%
64 1
 
< 0.1%
63 4
 
< 0.1%
62 3
 
< 0.1%

Nº do depósito
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing516490
Missing (%)100.0%
Memory size3.9 MiB

nº do documento configurável
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing516490
Missing (%)100.0%
Memory size3.9 MiB

Nº do roteiro operações
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct21585
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354389.96
Minimum0
Maximum1.000076 × 109
Zeros338514
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:36.281036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39381
95-th percentile28718
Maximum1.000076 × 109
Range1.000076 × 109
Interquartile range (IQR)9381

Descriptive statistics

Standard deviation18665484
Coefficient of variation (CV)52.669337
Kurtosis2864.4154
Mean354389.96
Median Absolute Deviation (MAD)0
Skewness53.538803
Sum1.8303887 × 1011
Variance3.484003 × 1014
MonotonicityNot monotonic
2023-04-01T15:24:36.380510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 338514
65.5%
24297 608
 
0.1%
18917 576
 
0.1%
16225 559
 
0.1%
21619 522
 
0.1%
1890 284
 
0.1%
132 264
 
0.1%
25899 260
 
0.1%
10355 218
 
< 0.1%
28151 217
 
< 0.1%
Other values (21575) 174468
33.8%
ValueCountFrequency (%)
0 338514
65.5%
7 4
 
< 0.1%
19 3
 
< 0.1%
68 3
 
< 0.1%
69 2
 
< 0.1%
70 9
 
< 0.1%
105 56
 
< 0.1%
125 61
 
< 0.1%
126 132
 
< 0.1%
128 6
 
< 0.1%
ValueCountFrequency (%)
1000075997 1
< 0.1%
1000074516 1
< 0.1%
1000071452 2
< 0.1%
1000062246 1
< 0.1%
1000056384 1
< 0.1%
1000055204 2
< 0.1%
1000054287 1
< 0.1%
1000051257 1
< 0.1%
1000051016 1
< 0.1%
1000050537 1
< 0.1%

Nº item reserva transferência
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct334
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1397936
Minimum0
Maximum346
Zeros386941
Zeros (%)74.9%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:36.486959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum346
Range346
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.4316015
Coefficient of variation (CV)5.6427773
Kurtosis1141.0647
Mean1.1397936
Median Absolute Deviation (MAD)0
Skewness28.852753
Sum588692
Variance41.365498
MonotonicityNot monotonic
2023-04-01T15:24:36.581670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 386941
74.9%
1 49103
 
9.5%
2 17700
 
3.4%
3 15326
 
3.0%
4 13303
 
2.6%
5 9544
 
1.8%
6 5204
 
1.0%
7 4699
 
0.9%
8 2830
 
0.5%
9 2032
 
0.4%
Other values (324) 9808
 
1.9%
ValueCountFrequency (%)
0 386941
74.9%
1 49103
 
9.5%
2 17700
 
3.4%
3 15326
 
3.0%
4 13303
 
2.6%
5 9544
 
1.8%
6 5204
 
1.0%
7 4699
 
0.9%
8 2830
 
0.5%
9 2032
 
0.4%
ValueCountFrequency (%)
346 1
< 0.1%
345 1
< 0.1%
344 1
< 0.1%
342 1
< 0.1%
341 1
< 0.1%
340 1
< 0.1%
339 1
< 0.1%
338 1
< 0.1%
337 1
< 0.1%
336 1
< 0.1%

Nº reserva
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26741
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183808.61
Minimum0
Maximum1574972
Zeros386941
Zeros (%)74.9%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:36.686930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39648
95-th percentile1111224
Maximum1574972
Range1574972
Interquartile range (IQR)9648

Descriptive statistics

Standard deviation391516.16
Coefficient of variation (CV)2.1300208
Kurtosis3.078384
Mean183808.61
Median Absolute Deviation (MAD)0
Skewness2.0726361
Sum9.4935309 × 1010
Variance1.532849 × 1011
MonotonicityNot monotonic
2023-04-01T15:24:36.785117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 386941
74.9%
1110689 464
 
0.1%
932750 438
 
0.1%
821186 428
 
0.1%
1014598 398
 
0.1%
217535 358
 
0.1%
31162 254
 
< 0.1%
10493 240
 
< 0.1%
1167678 195
 
< 0.1%
454189 161
 
< 0.1%
Other values (26731) 126613
 
24.5%
ValueCountFrequency (%)
0 386941
74.9%
66 4
 
< 0.1%
67 1
 
< 0.1%
206 5
 
< 0.1%
207 3
 
< 0.1%
208 4
 
< 0.1%
223 6
 
< 0.1%
224 3
 
< 0.1%
225 3
 
< 0.1%
226 3
 
< 0.1%
ValueCountFrequency (%)
1574972 12
< 0.1%
1574948 1
 
< 0.1%
1574925 4
 
< 0.1%
1574901 2
 
< 0.1%
1574900 1
 
< 0.1%
1574897 1
 
< 0.1%
1574895 2
 
< 0.1%
1574894 1
 
< 0.1%
1574893 1
 
< 0.1%
1574891 2
 
< 0.1%

Nota acomp.mercadoria
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing516490
Missing (%)100.0%
Memory size3.9 MiB

Operação
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)7.1%
Missing516476
Missing (%)> 99.9%
Memory size3.9 MiB
20.0
14 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters56
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20.0
2nd row20.0
3rd row20.0
4th row20.0
5th row20.0

Common Values

ValueCountFrequency (%)
20.0 14
 
< 0.1%
(Missing) 516476
> 99.9%

Length

2023-04-01T15:24:36.878839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:36.948336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
20.0 14
100.0%

Most occurring characters

ValueCountFrequency (%)
0 28
50.0%
2 14
25.0%
. 14
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42
75.0%
Other Punctuation 14
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
66.7%
2 14
33.3%
Other Punctuation
ValueCountFrequency (%)
. 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
50.0%
2 14
25.0%
. 14
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
50.0%
2 14
25.0%
. 14
25.0%

Ordem
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing222222
Missing (%)43.0%
Memory size3.9 MiB

Ordem do cliente
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing359478
Missing (%)69.6%
Memory size3.9 MiB

Ordem do cliente.1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2675
Distinct (%)1.4%
Missing331458
Missing (%)64.2%
Infinite0
Infinite (%)0.0%
Mean726697.92
Minimum3
Maximum60002383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:37.019695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile151
Q15683
median12641
Q317141
95-th percentile21235
Maximum60002383
Range60002380
Interquartile range (IQR)11458

Descriptive statistics

Standard deviation6511055.5
Coefficient of variation (CV)8.9597827
Kurtosis78.890755
Mean726697.92
Median Absolute Deviation (MAD)5570
Skewness8.9938764
Sum1.3446237 × 1011
Variance4.2393844 × 1013
MonotonicityNot monotonic
2023-04-01T15:24:37.116871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9638 1442
 
0.3%
19472 948
 
0.2%
1083 944
 
0.2%
2004 910
 
0.2%
19138 840
 
0.2%
15835 804
 
0.2%
18607 780
 
0.2%
21004 729
 
0.1%
5429 722
 
0.1%
2006 698
 
0.1%
Other values (2665) 176215
34.1%
(Missing) 331458
64.2%
ValueCountFrequency (%)
3 28
 
< 0.1%
4 28
 
< 0.1%
5 22
 
< 0.1%
6 2
 
< 0.1%
7 188
 
< 0.1%
8 118
 
< 0.1%
9 12
 
< 0.1%
10 24
 
< 0.1%
11 8
 
< 0.1%
12 520
0.1%
ValueCountFrequency (%)
60002383 4
 
< 0.1%
60002378 36
< 0.1%
60002377 88
< 0.1%
60002360 32
 
< 0.1%
60002158 3
 
< 0.1%
60002157 7
 
< 0.1%
60002156 3
 
< 0.1%
60002111 4
 
< 0.1%
60002108 16
 
< 0.1%
60002096 3
 
< 0.1%

Pedido
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10650
Distinct (%)20.8%
Missing465255
Missing (%)90.1%
Infinite0
Infinite (%)0.0%
Mean6.0112941 × 109
Minimum4.5 × 109
Maximum8.5000152 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2023-04-01T15:24:37.219437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.5 × 109
5-th percentile4.5000014 × 109
Q14.5000087 × 109
median7.0000014 × 109
Q37.0000043 × 109
95-th percentile7.0000063 × 109
Maximum8.5000152 × 109
Range4.0000152 × 109
Interquartile range (IQR)2.4999956 × 109

Descriptive statistics

Standard deviation1.2787606 × 109
Coefficient of variation (CV)0.21272635
Kurtosis-1.5770259
Mean6.0112941 × 109
Median Absolute Deviation (MAD)4753
Skewness-0.16380729
Sum3.0798865 × 1014
Variance1.6352287 × 1018
MonotonicityNot monotonic
2023-04-01T15:24:37.317029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7000004315 792
 
0.2%
7000004353 522
 
0.1%
7000004729 468
 
0.1%
7000000996 436
 
0.1%
7000004866 430
 
0.1%
7000000144 416
 
0.1%
7000004865 328
 
0.1%
7000005369 328
 
0.1%
7000002605 308
 
0.1%
7000004030 308
 
0.1%
Other values (10640) 46899
 
9.1%
(Missing) 465255
90.1%
ValueCountFrequency (%)
4500000001 5
 
< 0.1%
4500000005 1
 
< 0.1%
4500000018 1
 
< 0.1%
4500000022 1
 
< 0.1%
4500000026 1
 
< 0.1%
4500000029 11
< 0.1%
4500000030 20
< 0.1%
4500000031 3
 
< 0.1%
4500000032 6
 
< 0.1%
4500000033 1
 
< 0.1%
ValueCountFrequency (%)
8500015171 1
< 0.1%
8500015140 1
< 0.1%
8500015134 1
< 0.1%
8500015115 1
< 0.1%
8500015083 1
< 0.1%
8500015077 1
< 0.1%
8500015071 1
< 0.1%
8500015033 1
< 0.1%
8500015032 1
< 0.1%
8500015031 1
< 0.1%

Qtd.em UM pedido
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.9 MiB

Qtd.em UPP
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.9 MiB

Quantidade
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.9 MiB

Segmento de estoque
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing516490
Missing (%)100.0%
Memory size3.9 MiB

Subnº
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.4%
Missing516042
Missing (%)99.9%
Memory size3.9 MiB
*
387 
0
61 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters448
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row*
2nd row*
3rd row*
4th row*
5th row*

Common Values

ValueCountFrequency (%)
* 387
 
0.1%
0 61
 
< 0.1%
(Missing) 516042
99.9%

Length

2023-04-01T15:24:37.408042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:37.849501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
387
86.4%
0 61
 
13.6%

Most occurring characters

ValueCountFrequency (%)
* 387
86.4%
0 61
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 387
86.4%
Decimal Number 61
 
13.6%

Most frequent character per category

Other Punctuation
ValueCountFrequency (%)
* 387
100.0%
Decimal Number
ValueCountFrequency (%)
0 61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 448
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 387
86.4%
0 61
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 387
86.4%
0 61
 
13.6%

Texto cabeçalho documento
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct9
Distinct (%)0.1%
Missing507032
Missing (%)98.2%
Memory size3.9 MiB
Teste carga inicial
8033 
Cancellation of QM UD pos
1226 
Carga inicial
 
187
FB0006/22
 
3
FB0001/22
 
3
Other values (4)
 
6

Length

Max length25
Median length19
Mean length19.646437
Min length9

Characters and Unicode

Total characters185816
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowCancellation of QM UD pos
2nd rowCancellation of QM UD pos
3rd rowCancellation of QM UD pos
4th rowCancellation of QM UD pos
5th rowCancellation of QM UD pos

Common Values

ValueCountFrequency (%)
Teste carga inicial 8033
 
1.6%
Cancellation of QM UD pos 1226
 
0.2%
Carga inicial 187
 
< 0.1%
FB0006/22 3
 
< 0.1%
FB0001/22 3
 
< 0.1%
FB0005/22 3
 
< 0.1%
FB0004/22 1
 
< 0.1%
FB0003/22 1
 
< 0.1%
FB0002/22 1
 
< 0.1%
(Missing) 507032
98.2%

Length

2023-04-01T15:24:37.918986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:38.008072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
carga 8220
26.8%
inicial 8220
26.8%
teste 8033
26.2%
cancellation 1226
 
4.0%
of 1226
 
4.0%
qm 1226
 
4.0%
ud 1226
 
4.0%
pos 1226
 
4.0%
fb0006/22 3
 
< 0.1%
fb0001/22 3
 
< 0.1%
Other values (4) 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 27112
14.6%
i 25886
13.9%
21157
11.4%
c 17479
9.4%
e 17292
9.3%
l 10672
 
5.7%
n 10672
 
5.7%
s 9259
 
5.0%
t 9259
 
5.0%
g 8220
 
4.4%
Other values (20) 28808
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 150201
80.8%
Space Separator 21157
 
11.4%
Uppercase Letter 14374
 
7.7%
Decimal Number 72
 
< 0.1%
Other Punctuation 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 27112
18.1%
i 25886
17.2%
c 17479
11.6%
e 17292
11.5%
l 10672
 
7.1%
n 10672
 
7.1%
s 9259
 
6.2%
t 9259
 
6.2%
g 8220
 
5.5%
r 8220
 
5.5%
Other values (3) 6130
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
T 8033
55.9%
C 1413
 
9.8%
U 1226
 
8.5%
D 1226
 
8.5%
Q 1226
 
8.5%
M 1226
 
8.5%
F 12
 
0.1%
B 12
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 36
50.0%
2 25
34.7%
6 3
 
4.2%
1 3
 
4.2%
5 3
 
4.2%
4 1
 
1.4%
3 1
 
1.4%
Space Separator
ValueCountFrequency (%)
21157
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 164575
88.6%
Common 21241
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 27112
16.5%
i 25886
15.7%
c 17479
10.6%
e 17292
10.5%
l 10672
 
6.5%
n 10672
 
6.5%
s 9259
 
5.6%
t 9259
 
5.6%
g 8220
 
5.0%
r 8220
 
5.0%
Other values (11) 20504
12.5%
Common
ValueCountFrequency (%)
21157
99.6%
0 36
 
0.2%
2 25
 
0.1%
/ 12
 
0.1%
6 3
 
< 0.1%
1 3
 
< 0.1%
5 3
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 185816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 27112
14.6%
i 25886
13.9%
21157
11.4%
c 17479
9.4%
e 17292
9.3%
l 10672
 
5.7%
n 10672
 
5.7%
s 9259
 
5.0%
t 9259
 
5.0%
g 8220
 
4.4%
Other values (20) 28808
15.5%

Tipo de avaliação
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing516490
Missing (%)100.0%
Memory size3.9 MiB
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
WA
173171 
WR
167008 
WQ
102184 
WL
43312 
WE
29650 
Other values (2)
 
1165

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1032980
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWE
2nd rowWE
3rd rowWE
4th rowWE
5th rowWE

Common Values

ValueCountFrequency (%)
WA 173171
33.5%
WR 167008
32.3%
WQ 102184
19.8%
WL 43312
 
8.4%
WE 29650
 
5.7%
WI 1161
 
0.2%
WZ 4
 
< 0.1%

Length

2023-04-01T15:24:38.096741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T15:24:38.179783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
wa 173171
33.5%
wr 167008
32.3%
wq 102184
19.8%
wl 43312
 
8.4%
we 29650
 
5.7%
wi 1161
 
0.2%
wz 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
W 516490
50.0%
A 173171
 
16.8%
R 167008
 
16.2%
Q 102184
 
9.9%
L 43312
 
4.2%
E 29650
 
2.9%
I 1161
 
0.1%
Z 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1032980
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 516490
50.0%
A 173171
 
16.8%
R 167008
 
16.2%
Q 102184
 
9.9%
L 43312
 
4.2%
E 29650
 
2.9%
I 1161
 
0.1%
Z 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1032980
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 516490
50.0%
A 173171
 
16.8%
R 167008
 
16.2%
Q 102184
 
9.9%
L 43312
 
4.2%
E 29650
 
2.9%
I 1161
 
0.1%
Z 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1032980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 516490
50.0%
A 173171
 
16.8%
R 167008
 
16.2%
Q 102184
 
9.9%
L 43312
 
4.2%
E 29650
 
2.9%
I 1161
 
0.1%
Z 4
 
< 0.1%
Distinct104
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
SM para ordem
90153 
TE OrdClnt.:CQ p/lv.
52858 
TE ord.clnt.no cen.
51066 
TE qualidade p/livre
47512 
Entrada subproduto
35882 
Other values (99)
239019 

Length

Max length20
Median length19
Mean length17.152061
Min length6

Characters and Unicode

Total characters8858868
Distinct characters49
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowEM p/ClassCont.
2nd rowEM p/ClassCont.
3rd rowEM p/ClassCont.
4th rowEM p/ClassCont.
5th rowEM p/ClassCont.

Common Values

ValueCountFrequency (%)
SM para ordem 90153
17.5%
TE OrdClnt.:CQ p/lv. 52858
10.2%
TE ord.clnt.no cen. 51066
9.9%
TE qualidade p/livre 47512
9.2%
Entrada subproduto 35882
 
6.9%
FM fornmto ord.clnt. 27461
 
5.3%
TR transf.centro 27336
 
5.3%
EM p/EstqOrdCliente 26696
 
5.2%
EM para ordem 25483
 
4.9%
TE OrdClnt.p/OrdClnt 20664
 
4.0%
Other values (94) 111379
21.6%

Length

2023-04-01T15:24:38.268016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
te 181607
 
12.9%
para 144225
 
10.3%
ordem 122827
 
8.7%
sm 112042
 
8.0%
em 79268
 
5.6%
ordclnt.:cq 53462
 
3.8%
p/lv 53462
 
3.8%
ord.clnt.no 51066
 
3.6%
cen 51066
 
3.6%
qualidade 48606
 
3.5%
Other values (106) 508032
36.1%

Most occurring characters

ValueCountFrequency (%)
889173
 
10.0%
r 745879
 
8.4%
d 531737
 
6.0%
a 521462
 
5.9%
o 512257
 
5.8%
n 504959
 
5.7%
t 489603
 
5.5%
. 457450
 
5.2%
e 432097
 
4.9%
l 374943
 
4.2%
Other values (39) 3399308
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5727552
64.7%
Uppercase Letter 1530213
 
17.3%
Space Separator 889173
 
10.0%
Other Punctuation 711902
 
8.0%
Dash Punctuation 14
 
< 0.1%
Math Symbol 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 745879
13.0%
d 531737
9.3%
a 521462
9.1%
o 512257
8.9%
n 504959
8.8%
t 489603
8.5%
e 432097
7.5%
l 374943
 
6.5%
p 373976
 
6.5%
c 225071
 
3.9%
Other values (17) 1015568
17.7%
Uppercase Letter
ValueCountFrequency (%)
E 329459
21.5%
M 259906
17.0%
T 233182
15.2%
C 215786
14.1%
S 134090
8.8%
O 123988
 
8.1%
Q 54416
 
3.6%
F 49127
 
3.2%
R 42924
 
2.8%
D 40688
 
2.7%
Other values (6) 46647
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 457450
64.3%
/ 198675
27.9%
: 55777
 
7.8%
Space Separator
ValueCountFrequency (%)
889173
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Math Symbol
ValueCountFrequency (%)
> 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7257765
81.9%
Common 1601103
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 745879
 
10.3%
d 531737
 
7.3%
a 521462
 
7.2%
o 512257
 
7.1%
n 504959
 
7.0%
t 489603
 
6.7%
e 432097
 
6.0%
l 374943
 
5.2%
p 373976
 
5.2%
E 329459
 
4.5%
Other values (33) 2441393
33.6%
Common
ValueCountFrequency (%)
889173
55.5%
. 457450
28.6%
/ 198675
 
12.4%
: 55777
 
3.5%
- 14
 
< 0.1%
> 14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8852336
99.9%
None 6532
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
889173
 
10.0%
r 745879
 
8.4%
d 531737
 
6.0%
a 521462
 
5.9%
o 512257
 
5.8%
n 504959
 
5.7%
t 489603
 
5.5%
. 457450
 
5.2%
e 432097
 
4.9%
l 374943
 
4.2%
Other values (33) 3392776
38.3%
None
ValueCountFrequency (%)
ó 3600
55.1%
í 1375
 
21.1%
â 719
 
11.0%
ç 617
 
9.4%
á 156
 
2.4%
ã 65
 
1.0%

UM pedido
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct35
Distinct (%)< 0.1%
Missing418392
Missing (%)81.0%
Memory size3.9 MiB
KG
74745 
UN
12014 
H
 
4374
HRS
 
2770
UA
 
869
Other values (30)
 
3326

Length

Max length3
Median length2
Mean length1.9806112
Min length1

Characters and Unicode

Total characters194294
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowUA
2nd rowUA
3rd rowUA
4th rowUA
5th rowUA

Common Values

ValueCountFrequency (%)
KG 74745
 
14.5%
UN 12014
 
2.3%
H 4374
 
0.8%
HRS 2770
 
0.5%
UA 869
 
0.2%
PC 740
 
0.1%
M 716
 
0.1%
PAR 454
 
0.1%
M3 365
 
0.1%
L 270
 
0.1%
Other values (25) 781
 
0.2%
(Missing) 418392
81.0%

Length

2023-04-01T15:24:38.353085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kg 74745
76.2%
un 12014
 
12.2%
h 4374
 
4.5%
hrs 2770
 
2.8%
ua 869
 
0.9%
pc 740
 
0.8%
m 716
 
0.7%
par 454
 
0.5%
m3 365
 
0.4%
l 270
 
0.3%
Other values (25) 781
 
0.8%

Most occurring characters

ValueCountFrequency (%)
G 74836
38.5%
K 74745
38.5%
U 12883
 
6.6%
N 12018
 
6.2%
H 7148
 
3.7%
R 3432
 
1.8%
S 2794
 
1.4%
A 1365
 
0.7%
M 1242
 
0.6%
P 1226
 
0.6%
Other values (16) 2605
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 193717
99.7%
Decimal Number 465
 
0.2%
Lowercase Letter 112
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 74836
38.6%
K 74745
38.6%
U 12883
 
6.7%
N 12018
 
6.2%
H 7148
 
3.7%
R 3432
 
1.8%
S 2794
 
1.4%
A 1365
 
0.7%
M 1242
 
0.6%
P 1226
 
0.6%
Other values (12) 2028
 
1.0%
Decimal Number
ValueCountFrequency (%)
3 365
78.5%
2 100
 
21.5%
Lowercase Letter
ValueCountFrequency (%)
l 56
50.0%
t 56
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 193829
99.8%
Common 465
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 74836
38.6%
K 74745
38.6%
U 12883
 
6.6%
N 12018
 
6.2%
H 7148
 
3.7%
R 3432
 
1.8%
S 2794
 
1.4%
A 1365
 
0.7%
M 1242
 
0.6%
P 1226
 
0.6%
Other values (14) 2140
 
1.1%
Common
ValueCountFrequency (%)
3 365
78.5%
2 100
 
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 74836
38.5%
K 74745
38.5%
U 12883
 
6.6%
N 12018
 
6.2%
H 7148
 
3.7%
R 3432
 
1.8%
S 2794
 
1.4%
A 1365
 
0.7%
M 1242
 
0.6%
P 1226
 
0.6%
Other values (16) 2605
 
1.3%

Unid.medida básica
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct33
Distinct (%)< 0.1%
Missing1654
Missing (%)0.3%
Memory size3.9 MiB
KG
455141 
UN
 
32379
M
 
10869
H
 
4540
PAR
 
4096
Other values (28)
 
7811

Length

Max length3
Median length2
Mean length1.981802
Min length1

Characters and Unicode

Total characters1020303
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowH
2nd rowKG
3rd rowKG
4th rowKG
5th rowKG

Common Values

ValueCountFrequency (%)
KG 455141
88.1%
UN 32379
 
6.3%
M 10869
 
2.1%
H 4540
 
0.9%
PAR 4096
 
0.8%
HRS 2607
 
0.5%
L 1434
 
0.3%
PC 1048
 
0.2%
M3 637
 
0.1%
ROL 567
 
0.1%
Other values (23) 1518
 
0.3%
(Missing) 1654
 
0.3%

Length

2023-04-01T15:24:38.439252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kg 455141
88.4%
un 32379
 
6.3%
m 10869
 
2.1%
h 4540
 
0.9%
par 4096
 
0.8%
hrs 2607
 
0.5%
l 1434
 
0.3%
pc 1048
 
0.2%
m3 637
 
0.1%
rol 567
 
0.1%
Other values (23) 1518
 
0.3%

Most occurring characters

ValueCountFrequency (%)
G 455467
44.6%
K 455141
44.6%
N 32383
 
3.2%
U 32379
 
3.2%
M 12029
 
1.2%
R 7361
 
0.7%
H 7148
 
0.7%
P 5183
 
0.5%
A 4141
 
0.4%
S 2633
 
0.3%
Other values (16) 6438
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1019094
99.9%
Decimal Number 987
 
0.1%
Lowercase Letter 222
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 455467
44.7%
K 455141
44.7%
N 32383
 
3.2%
U 32379
 
3.2%
M 12029
 
1.2%
R 7361
 
0.7%
H 7148
 
0.7%
P 5183
 
0.5%
A 4141
 
0.4%
S 2633
 
0.3%
Other values (12) 5229
 
0.5%
Decimal Number
ValueCountFrequency (%)
3 637
64.5%
2 350
35.5%
Lowercase Letter
ValueCountFrequency (%)
l 111
50.0%
t 111
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1019316
99.9%
Common 987
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 455467
44.7%
K 455141
44.7%
N 32383
 
3.2%
U 32379
 
3.2%
M 12029
 
1.2%
R 7361
 
0.7%
H 7148
 
0.7%
P 5183
 
0.5%
A 4141
 
0.4%
S 2633
 
0.3%
Other values (14) 5451
 
0.5%
Common
ValueCountFrequency (%)
3 637
64.5%
2 350
35.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1020303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 455467
44.6%
K 455141
44.6%
N 32383
 
3.2%
U 32379
 
3.2%
M 12029
 
1.2%
R 7361
 
0.7%
H 7148
 
0.7%
P 5183
 
0.5%
A 4141
 
0.4%
S 2633
 
0.3%
Other values (16) 6438
 
0.6%

Unid.prç.pedido
Categorical

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)0.1%
Missing486840
Missing (%)94.3%
Memory size3.9 MiB
UN
10626 
KG
7715 
H
4494 
HRS
2650 
UA
 
869
Other values (31)
3296 

Length

Max length3
Median length2
Mean length1.9290725
Min length1

Characters and Unicode

Total characters57197
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowUA
2nd rowUA
3rd rowUA
4th rowUA
5th rowUA

Common Values

ValueCountFrequency (%)
UN 10626
 
2.1%
KG 7715
 
1.5%
H 4494
 
0.9%
HRS 2650
 
0.5%
UA 869
 
0.2%
PC 666
 
0.1%
M 663
 
0.1%
PAR 444
 
0.1%
M3 357
 
0.1%
L 272
 
0.1%
Other values (26) 894
 
0.2%
(Missing) 486840
94.3%

Length

2023-04-01T15:24:38.522829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
un 10626
35.8%
kg 7715
26.0%
h 4494
15.2%
hrs 2650
 
8.9%
ua 869
 
2.9%
pc 666
 
2.2%
m 663
 
2.2%
par 444
 
1.5%
m3 357
 
1.2%
l 272
 
0.9%
Other values (26) 894
 
3.0%

Most occurring characters

ValueCountFrequency (%)
U 11495
20.1%
N 10634
18.6%
G 7806
13.6%
K 7715
13.5%
H 7148
12.5%
R 3296
 
5.8%
S 2674
 
4.7%
A 1355
 
2.4%
M 1181
 
2.1%
P 1142
 
2.0%
Other values (16) 2751
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 56628
99.0%
Decimal Number 457
 
0.8%
Lowercase Letter 112
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 11495
20.3%
N 10634
18.8%
G 7806
13.8%
K 7715
13.6%
H 7148
12.6%
R 3296
 
5.8%
S 2674
 
4.7%
A 1355
 
2.4%
M 1181
 
2.1%
P 1142
 
2.0%
Other values (12) 2182
 
3.9%
Decimal Number
ValueCountFrequency (%)
3 357
78.1%
2 100
 
21.9%
Lowercase Letter
ValueCountFrequency (%)
l 56
50.0%
t 56
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56740
99.2%
Common 457
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 11495
20.3%
N 10634
18.7%
G 7806
13.8%
K 7715
13.6%
H 7148
12.6%
R 3296
 
5.8%
S 2674
 
4.7%
A 1355
 
2.4%
M 1181
 
2.1%
P 1142
 
2.0%
Other values (14) 2294
 
4.0%
Common
ValueCountFrequency (%)
3 357
78.1%
2 100
 
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 11495
20.1%
N 10634
18.6%
G 7806
13.6%
K 7715
13.5%
H 7148
12.5%
R 3296
 
5.8%
S 2674
 
4.7%
A 1355
 
2.4%
M 1181
 
2.1%
P 1142
 
2.0%
Other values (16) 2751
 
4.8%

Valor de venda
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.9 MiB

Valor PV com IVA
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size3.9 MiB

Interactions

2023-04-01T15:24:10.671916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:21.438982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:33.148340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:36.022098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:38.657139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:41.795522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:44.582494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:47.677340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:50.605175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:53.376030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:56.805210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:59.655900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:02.596982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:05.723967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:08.621962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:10.775678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:24.367289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:33.369936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:36.125208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:38.887174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:42.000700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:44.791908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:47.910893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:50.801968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:53.614532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:57.019458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:59.869417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:02.810543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:05.940416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:08.768923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:10.862160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:24.476817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:33.469627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:36.219355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:38.985505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:42.094502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:45.034910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:48.016205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:50.893160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:53.713345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:57.120255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:59.966922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:02.915595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:06.041045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:08.864516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:10.945603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:24.746822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:33.694917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:36.326504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:39.224821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:42.306511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:45.255278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:48.239250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:51.108765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:54.160085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:57.341763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:00.192544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:03.138629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:06.263330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:09.016972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:11.057441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:24.990153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:33.918282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:36.438605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:39.454741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:42.510345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:45.470224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:48.456159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:51.307617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:54.398984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:57.552252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:00.406497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:03.352869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:06.481824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:09.168948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:11.159321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:25.226904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:34.135830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:36.533406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:39.682168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:42.709290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:45.677756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:48.667754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:51.501364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:54.645464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:57.756790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:00.614940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:03.560663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:06.695744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:09.321969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:11.272957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:25.468946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:34.355411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:36.631449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:39.908551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:42.909344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:45.887536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:48.875830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:51.696933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:54.896713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:57.960285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:00.827862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:03.769466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:06.910383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:09.472935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:11.384929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:25.701248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:34.573160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:36.732091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:40.130271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:43.109391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:46.099066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:49.083833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:51.890165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:55.126626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:58.163814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:01.042747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:03.979686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:07.126392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:09.617194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:11.485009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:25.939048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:34.791495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:36.843640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:40.350994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:43.308936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:46.310572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:49.291469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:52.081882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:55.371952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:58.367419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:01.253273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:04.187225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:07.346943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:09.771884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:11.586192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:26.168866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:35.010986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:36.949954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:40.587869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:43.510412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:46.521082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:49.501840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:52.277772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:55.623693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:58.567341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:01.467446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:04.395863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:07.562876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:09.922744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:11.697320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:26.404510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:35.236792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:37.173502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:40.834625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:43.716995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:46.739949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:49.719900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:52.482796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:55.859041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:58.774798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:01.686126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:04.611239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:07.785282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:10.075365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:11.808752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:32.418738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:35.456601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:37.278024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:41.069836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:43.920095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:46.959268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:49.931750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:52.680094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:56.095009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:58.981367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:01.901511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:04.821706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:08.001860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:10.223461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:11.921143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:32.662506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:35.680914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:37.381271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:41.315953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:44.124994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:47.179485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:50.148988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:52.882989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:56.335532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:59.189857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:02.122523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:05.038109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:08.223805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:10.362591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:12.018734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:32.813955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:35.825684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:37.489243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:41.474597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:44.261460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:47.327923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:50.294912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:53.022817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:56.481964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:59.333392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:02.265271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:05.400870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:08.368742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:10.499650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:12.093844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:32.919971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:35.923448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:37.584918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:41.579209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:44.367972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:47.428919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:50.391901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:53.136889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:56.591243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:23:59.436137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:02.369126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:05.498800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:08.466899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-04-01T15:24:10.592067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-04-01T15:24:38.890426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Doc.materialItem doc.materialClienteContadorItemItem ord.clienteItem ord.cliente.1Linha originalMaterial AlteradoMotivo do movimentoNº do roteiro operaçõesNº item reserva transferênciaNº reservaOrdem do cliente.1PedidoDescrição motivo movimentoDepósitoEstoque especialUM registroAno doc.materialCentro custoCód.débito/créditoCódigo de movimentoConsumoElemento PEPImobilizadoClassificaçãoSubnºTexto cabeçalho documentoTipo de operaçãoUM pedidoUnid.medida básicaUnid.prç.pedido
Doc.material1.000-0.0990.139-0.1690.306-0.011-0.0100.0450.1020.015-0.052-0.191-0.1210.962-0.2130.9660.4870.6770.6270.0320.6820.2121.0000.5220.5321.0000.4381.0001.0001.0000.7700.6091.000
Item doc.material-0.0991.000-0.081-0.1640.1590.004-0.089-0.021-0.078-0.023-0.0750.1120.0710.0010.2641.0000.0891.0000.0470.6270.0950.0580.0271.0001.0001.0000.0621.0000.2350.0680.0000.0451.000
Cliente0.139-0.0811.000NaN-0.0070.1440.162NaN0.112NaN0.0030.0030.0030.155NaN0.0000.1501.0000.2370.1260.0000.1621.0001.0000.0000.0000.1480.0001.0000.128NaN0.2370.000
Contador-0.169-0.164NaN1.000-0.326-0.260-0.168-0.027-0.367-0.0230.6550.3200.2940.027NaN1.0000.2210.0140.0450.0601.0000.1220.3011.0001.0001.0000.1001.0000.1850.1490.0780.0441.000
Item0.3060.159-0.007-0.3261.000-0.214-0.2400.077-0.0130.007-0.233-0.189-0.189NaN0.3060.0000.2611.0000.0310.0220.2760.0680.1440.0220.0000.9380.0570.1101.0000.0990.0390.0320.216
Item ord.cliente-0.0110.0040.144-0.260-0.2141.0000.853-0.0180.483-0.015-0.455-0.369-0.369-0.003NaN1.0000.0850.0310.0280.0301.0000.0360.2020.1551.0001.0000.1001.0000.0940.0791.0000.0281.000
Item ord.cliente.1-0.010-0.0890.162-0.168-0.2400.8531.000-0.0200.541-0.017-0.279-0.413-0.413-0.023NaN1.0000.0850.0350.0310.0340.0000.0150.1220.1551.0001.0000.1081.0000.0940.0580.0350.0301.000
Linha original0.045-0.021NaN-0.0270.077-0.018-0.0201.000-0.021-0.001-0.019-0.016-0.016NaN-0.0741.0001.0001.0000.0350.0000.0800.0050.0170.1281.0000.0000.0260.0001.0000.0120.0330.0350.014
Material Alterado0.102-0.0780.112-0.367-0.0130.4830.541-0.0211.000-0.020-0.465-0.347-0.3180.176-0.2920.3310.5480.9990.4560.0950.4260.1080.4910.4510.4380.4370.6880.7570.4130.2660.4640.3780.463
Motivo do movimento0.015-0.023NaN-0.0230.007-0.015-0.017-0.001-0.0201.000-0.017-0.013-0.013NaN-0.0290.9300.0121.0000.0000.0051.0000.0171.0001.0001.0001.0000.0141.0001.0000.0091.0000.0001.000
Nº do roteiro operações-0.052-0.0750.0030.655-0.233-0.455-0.279-0.019-0.465-0.0171.0000.4880.4970.046NaN1.0000.0961.0000.0750.0081.0000.0171.0001.0001.0001.0000.0811.0001.0000.0261.0000.0751.000
Nº item reserva transferência-0.1910.1120.0030.320-0.189-0.369-0.413-0.016-0.347-0.0130.4881.0000.973-0.005NaN1.0000.0720.3190.0580.0100.0720.0391.0000.1841.0001.0000.0601.0001.0000.0241.0000.0581.000
Nº reserva-0.1210.0710.0030.294-0.189-0.369-0.413-0.016-0.318-0.0130.4970.9731.000-0.005NaN1.0000.1710.3060.1130.2580.2290.2081.0000.1601.0001.0000.1841.0001.0000.2151.0000.1131.000
Ordem do cliente.10.9620.0010.1550.027NaN-0.003-0.023NaN0.176NaN0.046-0.005-0.0051.000NaN0.0000.8401.0001.0000.0191.0000.0000.1001.0000.0000.0000.1880.0001.0000.0701.0001.0000.000
Pedido-0.2130.264NaNNaN0.306NaNNaN-0.074-0.292-0.029NaNNaNNaNNaN1.0000.0000.6241.0000.4630.1190.6840.3680.7340.0590.9060.6940.5270.2320.5610.5460.4600.3890.458
Descrição motivo movimento0.9661.0000.0001.0000.0001.0001.0001.0000.3310.9301.0001.0001.0000.0000.0001.0000.6110.0000.3500.5370.7750.9181.0001.000NaN0.0000.6520.0000.0000.9660.1280.4060.128
Depósito0.4870.0890.1500.2210.2610.0850.0851.0000.5480.0120.0960.0720.1710.8400.6240.6111.0001.0000.2690.1760.2170.1980.9690.8590.5851.0000.6761.0000.4870.4050.2750.2640.295
Estoque especial0.6771.0001.0000.0141.0000.0310.0351.0000.9991.0001.0000.3190.3061.0001.0000.0001.0001.0000.9530.0311.0000.0001.0000.9871.0000.0001.0000.0001.0000.6790.9520.9531.000
UM registro0.6270.0470.2370.0450.0310.0280.0310.0350.4560.0000.0750.0580.1131.0000.4630.3500.2690.9531.0000.0670.2480.1500.5520.2380.4890.2760.5410.7630.1150.2820.9970.9660.982
Ano doc.material0.0320.6270.1260.0600.0220.0300.0340.0000.0950.0050.0080.0100.2580.0190.1190.5370.1760.0310.0671.0000.2190.0630.0320.0900.8150.0000.0890.1320.7070.1460.0890.0630.158
Centro custo0.6820.0950.0001.0000.2761.0000.0000.0800.4261.0001.0000.0720.2291.0000.6840.7750.2171.0000.2480.2191.0000.6241.0000.2020.0001.0000.4181.0000.0000.6070.2390.2240.240
Cód.débito/crédito0.2120.0580.1620.1220.0680.0360.0150.0050.1080.0170.0170.0390.2080.0000.3680.9180.1980.0000.1500.0630.6241.0000.7770.2530.3410.0000.1710.0000.1760.2830.0550.1460.106
Código de movimento1.0000.0271.0000.3010.1440.2020.1220.0170.4911.0001.0001.0001.0000.1000.7341.0000.9691.0000.5520.0321.0000.7771.0000.0321.0001.0000.6151.0001.0001.0000.5500.5431.000
Consumo0.5221.0001.0001.0000.0220.1550.1550.1280.4511.0001.0000.1840.1601.0000.0591.0000.8590.9870.2380.0900.2020.2530.0321.0000.9060.7700.6880.8360.0000.5310.1610.2420.161
Elemento PEP0.5321.0000.0001.0000.0001.0001.0001.0000.4381.0001.0001.0001.0000.0000.906NaN0.5851.0000.4890.8150.0000.3411.0000.9061.0000.0000.4760.0000.0000.5320.5090.4880.509
Imobilizado1.0001.0000.0001.0000.9381.0001.0000.0000.4371.0001.0001.0001.0000.0000.6940.0001.0000.0000.2760.0001.0000.0001.0000.7700.0001.0000.4930.9380.0001.0000.2760.2760.276
Classificação0.4380.0620.1480.1000.0570.1000.1080.0260.6880.0140.0810.0600.1840.1880.5270.6520.6761.0000.5410.0890.4180.1710.6150.6880.4760.4931.0000.8020.3730.2890.5090.4850.494
Subnº1.0001.0000.0001.0000.1101.0001.0000.0000.7571.0001.0001.0001.0000.0000.2320.0001.0000.0000.7630.1321.0000.0001.0000.8360.0000.9380.8021.0000.0001.0000.7630.7630.763
Texto cabeçalho documento1.0000.2351.0000.1851.0000.0940.0941.0000.4131.0001.0001.0001.0001.0000.5610.0000.4871.0000.1150.7070.0000.1761.0000.0000.0000.0000.3730.0001.0001.0001.0000.1141.000
Tipo de operação1.0000.0680.1280.1490.0990.0790.0580.0120.2660.0090.0260.0240.2150.0700.5460.9660.4050.6790.2820.1460.6070.2831.0000.5310.5321.0000.2891.0001.0001.0000.5500.2761.000
UM pedido0.7700.000NaN0.0780.0391.0000.0350.0330.4641.0001.0001.0001.0001.0000.4600.1280.2750.9520.9970.0890.2390.0550.5500.1610.5090.2760.5090.7631.0000.5501.0000.9740.985
Unid.medida básica0.6090.0450.2370.0440.0320.0280.0300.0350.3780.0000.0750.0580.1131.0000.3890.4060.2640.9530.9660.0630.2240.1460.5430.2420.4880.2760.4850.7630.1140.2760.9741.0000.976
Unid.prç.pedido1.0001.0000.0001.0000.2161.0001.0000.0140.4631.0001.0001.0001.0000.0000.4580.1280.2951.0000.9820.1580.2400.1061.0000.1610.5090.2760.4940.7631.0001.0000.9850.9761.000

Missing values

2023-04-01T15:24:14.644577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-01T15:24:18.316809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-01T15:24:26.910835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Descrição motivo movimentoCentroDepósitoTipo de movimentoEstoque especialDoc.materialItem doc.materialData de lançamentoQtd. UM registroUM registroAno doc.materialCentro custoClassificação contábil múltiplaClienteCód.débito/créditoCódigo de movimentoCódigo entradaConsumoContadorData de entradaData do documentoDiagrama de redeDivisão ord.clienteDocumento do depósitoElemento PEPEmpresaFornecedorHora do registroImobilizadoItemItem gerado automaticamenteItem ord.clienteItem ord.cliente.1Linha originalLoteMaterial AlteradoClassificaçãoMoedaMontante em MIMontante externo em MIMotivo do movimentoNº do depósitonº do documento configurávelNº do roteiro operaçõesNº item reserva transferênciaNº reservaNota acomp.mercadoriaOperaçãoOrdemOrdem do clienteOrdem do cliente.1PedidoQtd.em UM pedidoQtd.em UPPQuantidadeSegmento de estoqueSubnºTexto cabeçalho documentoTipo de avaliaçãoTipo de operaçãoTxt.tipo movimentoUM pedidoUnid.medida básicaUnid.prç.pedidoValor de vendaValor PV com IVA
0NaNA001NaN101NaN5000023575124/08/20221UA2022A001ALM301NaNNaNSBNaNV024/08/202224/08/2022NaN0NaNNaN1000100116808:21:57NaN10NaN000NaN0##BRL7.156,970,000NaNNaN000NaNNaNNaNNaNNaN8.500006e+09110,000NaNNaNNaNNaNWEEM p/ClassCont.UANaNUA0,000,00
1NaNA001NaN101NaN5000023617124/08/20221UA2022A001ALM301NaNNaNSBNaNV024/08/202224/08/2022NaN0NaNNaN1000100116809:27:09NaN10NaN000NaN0##BRL7.156,830,000NaNNaN000NaNNaNNaNNaNNaN8.500006e+09110,000NaNNaNNaNNaNWEEM p/ClassCont.UANaNUA0,000,00
2NaNA001NaN101NaN5000023590124/08/20221UA2022A001ALM301NaNNaNSBNaNV024/08/202224/08/2022NaN0NaNNaN1000100116808:27:52NaN10NaN000NaN0##BRL7.156,890,000NaNNaN000NaNNaNNaNNaNNaN8.500006e+09110,000NaNNaNNaNNaNWEEM p/ClassCont.UANaNUA0,000,00
3NaNA001NaN101NaN5000023576124/08/20221UA2022A001ALM301NaNNaNSBNaNV024/08/202224/08/2022NaN0NaNNaN1000100116808:25:24NaN10NaN000NaN0##BRL7.156,740,000NaNNaN000NaNNaNNaNNaNNaN8.500006e+09110,000NaNNaNNaNNaNWEEM p/ClassCont.UANaNUA0,000,00
4NaNA001NaN101NaN5000023567123/08/20221UA2022A001ALM301NaNNaNSBNaNV023/08/202223/08/2022NaN0NaNNaN1000100116817:50:31NaN10NaN000NaN0##BRL7.157,450,000NaNNaN000NaNNaNNaNNaNNaN8.500006e+09110,000NaNNaNNaNNaNWEEM p/ClassCont.UANaNUA0,000,00
5NaNA001NaN101NaN5000023568123/08/20221UA2022A001ALM301NaNNaNSBNaNV023/08/202223/08/2022NaN0NaNNaN1000100116817:53:06NaN10NaN000NaN0##BRL7.156,990,000NaNNaN000NaNNaNNaNNaNNaN8.500006e+09110,000NaNNaNNaNNaNWEEM p/ClassCont.UANaNUA0,000,00
6NaNA001NaN101NaN5000023570123/08/20221UA2022A001ALM301NaNNaNSBNaNV023/08/202223/08/2022NaN0NaNNaN1000100116817:58:56NaN10NaN000NaN0##BRL7.149,860,000NaNNaN000NaNNaNNaNNaNNaN8.500006e+09110,000NaNNaNNaNNaNWEEM p/ClassCont.UANaNUA0,000,00
7NaNA001NaN101NaN5000023569123/08/20221UA2022A001ALM301NaNNaNSBNaNV023/08/202223/08/2022NaN0NaNNaN1000100116817:55:02NaN10NaN000NaN0##BRL7.156,990,000NaNNaN000NaNNaNNaNNaNNaN8.500006e+09110,000NaNNaNNaNNaNWEEM p/ClassCont.UANaNUA0,000,00
8NaNA001NaN101NaN5000023591124/08/20221UA2022A001ALM301NaNNaNSBNaNV024/08/202224/08/2022NaN0NaNNaN1000100116808:30:15NaN10NaN000NaN0##BRL6.438,890,000NaNNaN000NaNNaNNaNNaNNaN8.500006e+09110,000NaNNaNNaNNaNWEEM p/ClassCont.UANaNUA0,000,00
9NaNA001NaN101NaN5000023595124/08/20221UA2022A001ALM301NaNNaNSBNaNV024/08/202224/08/2022NaN0NaNNaN1000100116808:33:14NaN10NaN000NaN0##BRL6.437,930,000NaNNaN000NaNNaNNaNNaNNaN8.500006e+09110,000NaNNaNNaNNaNWEEM p/ClassCont.UANaNUA0,000,00
Descrição motivo movimentoCentroDepósitoTipo de movimentoEstoque especialDoc.materialItem doc.materialData de lançamentoQtd. UM registroUM registroAno doc.materialCentro custoClassificação contábil múltiplaClienteCód.débito/créditoCódigo de movimentoCódigo entradaConsumoContadorData de entradaData do documentoDiagrama de redeDivisão ord.clienteDocumento do depósitoElemento PEPEmpresaFornecedorHora do registroImobilizadoItemItem gerado automaticamenteItem ord.clienteItem ord.cliente.1Linha originalLoteMaterial AlteradoClassificaçãoMoedaMontante em MIMontante externo em MIMotivo do movimentoNº do depósitonº do documento configurávelNº do roteiro operaçõesNº item reserva transferênciaNº reservaNota acomp.mercadoriaOperaçãoOrdemOrdem do clienteOrdem do cliente.1PedidoQtd.em UM pedidoQtd.em UPPQuantidadeSegmento de estoqueSubnºTexto cabeçalho documentoTipo de avaliaçãoTipo de operaçãoTxt.tipo movimentoUM pedidoUnid.medida básicaUnid.prç.pedidoValor de vendaValor PV com IVA
516480NaNA001NaN101NaN5000006822111/04/20221HRS2022A005ATR112NaNNaNSBNaNV04466244662NaN0NaNNaN100010144970,399236111NaN10NaN000NaN2183Materia primaBRL654,7600NaNNaN000NaNNaNNaNNaNNaN4.500003e+09111NaNNaNNaNNaNWEEM p/ClassCont.HRSHRSHRS00
516481NaNA001NaN101NaN5000006823111/04/20221HRS2022A005ATR112NaNNaNSBNaNV04466244662NaN0NaNNaN100010144970,401574074NaN20NaN000NaN2183Materia primaBRL654,7600NaNNaN000NaNNaNNaNNaNNaN4.500003e+09111NaNNaNNaNNaNWEEM p/ClassCont.HRSHRSHRS00
516482NaNA001NaN101NaN5000006824111/04/20221HRS2022A005ATR112NaNNaNSBNaNV04466244662NaN0NaNNaN100010144970,403113426NaN30NaN000NaN2183Materia primaBRL654,7600NaNNaN000NaNNaNNaNNaNNaN4.500003e+09111NaNNaNNaNNaNWEEM p/ClassCont.HRSHRSHRS00
516483NaNA001NaN101NaN5000006825111/04/20221HRS2022A005ATR112NaNNaNSBNaNV04466244662NaN0NaNNaN100010144970,404178241NaN40NaN000NaN2183Materia primaBRL654,7600NaNNaN000NaNNaNNaNNaNNaN4.500003e+09111NaNNaNNaNNaNWEEM p/ClassCont.HRSHRSHRS00
516484NaNA001NaN101NaN5000006736108/04/20221HRS2022A001ALM112NaNNaNSBNaNV04465944659NaN0NaNNaN100010003660,46380787NaN10NaN000NaN2183Materia primaBRL662500NaNNaN000NaNNaNNaNNaNNaN4.500003e+09111NaNNaNNaNNaNWEEM p/ClassCont.HRSHRSHRS00
516485NaNA001NaN101NaN5000006736208/04/20221HRS2022A001ALM112NaNNaNSBNaNV04465944659NaN0NaNNaN100010003660,46380787NaN20NaN000NaN2183Materia primaBRL662500NaNNaN000NaNNaNNaNNaNNaN4.500003e+09111NaNNaNNaNNaNWEEM p/ClassCont.HRSHRSHRS00
516486NaNA001NaN101NaN5000006736308/04/20221HRS2022A001ALM112NaNNaNSBNaNV04465944659NaN0NaNNaN100010003660,46380787NaN30NaN000NaN2183Materia primaBRL662500NaNNaN000NaNNaNNaNNaNNaN4.500003e+09111NaNNaNNaNNaNWEEM p/ClassCont.HRSHRSHRS00
516487NaNA001NaN101NaN5000006736408/04/20221HRS2022A001ALM112NaNNaNSBNaNV04465944659NaN0NaNNaN100010003660,46380787NaN40NaN000NaN2183Materia primaBRL662500NaNNaN000NaNNaNNaNNaNNaN4.500003e+09111NaNNaNNaNNaNWEEM p/ClassCont.HRSHRSHRS00
516488NaNA001NaN101NaN5000006743108/04/20224HRS2022A005ATR112NaNNaNSBNaNV04465944659NaN0NaNNaN100010144970,452986111NaN10NaN000NaN2183Materia primaBRL2619,0400NaNNaN000NaNNaNNaNNaNNaN4.500003e+09444NaNNaNNaNNaNWEEM p/ClassCont.HRSHRSHRS00
516489NaNA001NaN101NaN5000006052131/03/20221HRS2022*XNaNSBNaNV04465144651*0NaNNaN100010015180,713715278*10NaN001NaN2183Materia primaBRL3183,5500NaNNaN000NaNNaN**NaN4.500003e+09111NaN*NaNNaNWEEM p/ClassCont.HRSHRSHRS00